automl

package
v0.0.0-...-e165f0f Latest Latest
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 Go to latest Published: Sep 1, 2020 License: Apache-2.0 Imports: 16 Imported by: 0

Documentation

Index

Constants

This section is empty.

Variables

View Source 
var ClassificationType_name = map[int32]string{
	0: "CLASSIFICATION_TYPE_UNSPECIFIED",
	1: "MULTICLASS",
	2: "MULTILABEL",
}
View Source 
var ClassificationType_value = map[string]int32{
	"CLASSIFICATION_TYPE_UNSPECIFIED": 0,
	"MULTICLASS":                      1,
	"MULTILABEL":                      2,
}
View Source 
var DocumentDimensions_DocumentDimensionUnit_name = map[int32]string{
	0: "DOCUMENT_DIMENSION_UNIT_UNSPECIFIED",
	1: "INCH",
	2: "CENTIMETER",
	3: "POINT",
}
View Source 
var DocumentDimensions_DocumentDimensionUnit_value = map[string]int32{
	"DOCUMENT_DIMENSION_UNIT_UNSPECIFIED": 0,
	"INCH":                                1,
	"CENTIMETER":                          2,
	"POINT":                               3,
}
View Source 
var Document_Layout_TextSegmentType_name = map[int32]string{
	0: "TEXT_SEGMENT_TYPE_UNSPECIFIED",
	1: "TOKEN",
	2: "PARAGRAPH",
	3: "FORM_FIELD",
	4: "FORM_FIELD_NAME",
	5: "FORM_FIELD_CONTENTS",
	6: "TABLE",
	7: "TABLE_HEADER",
	8: "TABLE_ROW",
	9: "TABLE_CELL",
}
View Source 
var Document_Layout_TextSegmentType_value = map[string]int32{
	"TEXT_SEGMENT_TYPE_UNSPECIFIED": 0,
	"TOKEN":                         1,
	"PARAGRAPH":                     2,
	"FORM_FIELD":                    3,
	"FORM_FIELD_NAME":               4,
	"FORM_FIELD_CONTENTS":           5,
	"TABLE":                         6,
	"TABLE_HEADER":                  7,
	"TABLE_ROW":                     8,
	"TABLE_CELL":                    9,
}
View Source 
var Model_DeploymentState_name = map[int32]string{
	0: "DEPLOYMENT_STATE_UNSPECIFIED",
	1: "DEPLOYED",
	2: "UNDEPLOYED",
}
View Source 
var Model_DeploymentState_value = map[string]int32{
	"DEPLOYMENT_STATE_UNSPECIFIED": 0,
	"DEPLOYED":                     1,
	"UNDEPLOYED":                   2,
}
View Source 
var TypeCode_name = map[int32]string{
	0:  "TYPE_CODE_UNSPECIFIED",
	3:  "FLOAT64",
	4:  "TIMESTAMP",
	6:  "STRING",
	8:  "ARRAY",
	9:  "STRUCT",
	10: "CATEGORY",
}
View Source 
var TypeCode_value = map[string]int32{
	"TYPE_CODE_UNSPECIFIED": 0,
	"FLOAT64":               3,
	"TIMESTAMP":             4,
	"STRING":                6,
	"ARRAY":                 8,
	"STRUCT":                9,
	"CATEGORY":              10,
}

Functions

func RegisterAutoMlServer 

func RegisterAutoMlServer(s *grpc.Server, srv AutoMlServer)

func RegisterPredictionServiceServer 

func RegisterPredictionServiceServer(s *grpc.Server, srv PredictionServiceServer)

Types

type AnnotationPayload 

type AnnotationPayload struct {
	// Output only . Additional information about the annotation
	// specific to the AutoML domain.
	//
	// Types that are valid to be assigned to Detail:
	//	*AnnotationPayload_Translation
	//	*AnnotationPayload_Classification
	//	*AnnotationPayload_ImageObjectDetection
	//	*AnnotationPayload_VideoClassification
	//	*AnnotationPayload_VideoObjectTracking
	//	*AnnotationPayload_TextExtraction
	//	*AnnotationPayload_TextSentiment
	//	*AnnotationPayload_Tables
	Detail isAnnotationPayload_Detail `protobuf_oneof:"detail"`
	// Output only . The resource ID of the annotation spec that
	// this annotation pertains to. The annotation spec comes from either an
	// ancestor dataset, or the dataset that was used to train the model in use.
	AnnotationSpecId string `protobuf:"bytes,1,opt,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// Output only. The value of
	// [display_name][google.cloud.automl.v1beta1.AnnotationSpec.display_name]
	// when the model was trained. Because this field returns a value at model
	// training time, for different models trained using the same dataset, the
	// returned value could be different as model owner could update the
	// `display_name` between any two model training.
	DisplayName          string   `protobuf:"bytes,5,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Contains annotation information that is relevant to AutoML.

func (*AnnotationPayload) Descriptor 

func (*AnnotationPayload) Descriptor() ([]byte, []int)

func (*AnnotationPayload) GetAnnotationSpecId 

func (m *AnnotationPayload) GetAnnotationSpecId() string

func (*AnnotationPayload) GetClassification 

func (m *AnnotationPayload) GetClassification() *ClassificationAnnotation

func (*AnnotationPayload) GetDetail 

func (m *AnnotationPayload) GetDetail() isAnnotationPayload_Detail

func (*AnnotationPayload) GetDisplayName 

func (m *AnnotationPayload) GetDisplayName() string

func (*AnnotationPayload) GetImageObjectDetection 

func (m *AnnotationPayload) GetImageObjectDetection() *ImageObjectDetectionAnnotation

func (*AnnotationPayload) GetTables 

func (m *AnnotationPayload) GetTables() *TablesAnnotation

func (*AnnotationPayload) GetTextExtraction 

func (m *AnnotationPayload) GetTextExtraction() *TextExtractionAnnotation

func (*AnnotationPayload) GetTextSentiment 

func (m *AnnotationPayload) GetTextSentiment() *TextSentimentAnnotation

func (*AnnotationPayload) GetTranslation 

func (m *AnnotationPayload) GetTranslation() *TranslationAnnotation

func (*AnnotationPayload) GetVideoClassification 

func (m *AnnotationPayload) GetVideoClassification() *VideoClassificationAnnotation

func (*AnnotationPayload) GetVideoObjectTracking 

func (m *AnnotationPayload) GetVideoObjectTracking() *VideoObjectTrackingAnnotation

func (*AnnotationPayload) ProtoMessage 

func (*AnnotationPayload) ProtoMessage()

func (*AnnotationPayload) Reset 

func (m *AnnotationPayload) Reset()

func (*AnnotationPayload) String 

func (m *AnnotationPayload) String() string

func (*AnnotationPayload) XXX_DiscardUnknown 

func (m *AnnotationPayload) XXX_DiscardUnknown()

func (*AnnotationPayload) XXX_Marshal 

func (m *AnnotationPayload) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*AnnotationPayload) XXX_Merge 

func (m *AnnotationPayload) XXX_Merge(src proto.Message)

func (*AnnotationPayload) XXX_OneofWrappers 

func (*AnnotationPayload) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*AnnotationPayload) XXX_Size 

func (m *AnnotationPayload) XXX_Size() int

func (*AnnotationPayload) XXX_Unmarshal 

func (m *AnnotationPayload) XXX_Unmarshal(b []byte) error

type AnnotationPayload_Classification 

type AnnotationPayload_Classification struct {
	Classification *ClassificationAnnotation `protobuf:"bytes,3,opt,name=classification,proto3,oneof"`
}

type AnnotationPayload_ImageObjectDetection 

type AnnotationPayload_ImageObjectDetection struct {
	ImageObjectDetection *ImageObjectDetectionAnnotation `protobuf:"bytes,4,opt,name=image_object_detection,json=imageObjectDetection,proto3,oneof"`
}

type AnnotationPayload_Tables 

type AnnotationPayload_Tables struct {
	Tables *TablesAnnotation `protobuf:"bytes,10,opt,name=tables,proto3,oneof"`
}

type AnnotationPayload_TextExtraction 

type AnnotationPayload_TextExtraction struct {
	TextExtraction *TextExtractionAnnotation `protobuf:"bytes,6,opt,name=text_extraction,json=textExtraction,proto3,oneof"`
}

type AnnotationPayload_TextSentiment 

type AnnotationPayload_TextSentiment struct {
	TextSentiment *TextSentimentAnnotation `protobuf:"bytes,7,opt,name=text_sentiment,json=textSentiment,proto3,oneof"`
}

type AnnotationPayload_Translation 

type AnnotationPayload_Translation struct {
	Translation *TranslationAnnotation `protobuf:"bytes,2,opt,name=translation,proto3,oneof"`
}

type AnnotationPayload_VideoClassification 

type AnnotationPayload_VideoClassification struct {
	VideoClassification *VideoClassificationAnnotation `protobuf:"bytes,9,opt,name=video_classification,json=videoClassification,proto3,oneof"`
}

type AnnotationPayload_VideoObjectTracking 

type AnnotationPayload_VideoObjectTracking struct {
	VideoObjectTracking *VideoObjectTrackingAnnotation `protobuf:"bytes,8,opt,name=video_object_tracking,json=videoObjectTracking,proto3,oneof"`
}

type AnnotationSpec 

type AnnotationSpec struct {
	// Output only. Resource name of the annotation spec.
	// Form:
	//
	// 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The name of the annotation spec to show in the interface. The name can be
	// up to 32 characters long and must match the regexp `[a-zA-Z0-9_]+`.
	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only. The number of examples in the parent dataset
	// labeled by the annotation spec.
	ExampleCount         int32    `protobuf:"varint,9,opt,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A definition of an annotation spec.

func (*AnnotationSpec) Descriptor 

func (*AnnotationSpec) Descriptor() ([]byte, []int)

func (*AnnotationSpec) GetDisplayName 

func (m *AnnotationSpec) GetDisplayName() string

func (*AnnotationSpec) GetExampleCount 

func (m *AnnotationSpec) GetExampleCount() int32

func (*AnnotationSpec) GetName 

func (m *AnnotationSpec) GetName() string

func (*AnnotationSpec) ProtoMessage 

func (*AnnotationSpec) ProtoMessage()

func (*AnnotationSpec) Reset 

func (m *AnnotationSpec) Reset()

func (*AnnotationSpec) String 

func (m *AnnotationSpec) String() string

func (*AnnotationSpec) XXX_DiscardUnknown 

func (m *AnnotationSpec) XXX_DiscardUnknown()

func (*AnnotationSpec) XXX_Marshal 

func (m *AnnotationSpec) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*AnnotationSpec) XXX_Merge 

func (m *AnnotationSpec) XXX_Merge(src proto.Message)

func (*AnnotationSpec) XXX_Size 

func (m *AnnotationSpec) XXX_Size() int

func (*AnnotationSpec) XXX_Unmarshal 

func (m *AnnotationSpec) XXX_Unmarshal(b []byte) error

type ArrayStats 

type ArrayStats struct {
	// Stats of all the values of all arrays, as if they were a single long
	// series of data. The type depends on the element type of the array.
	MemberStats          *DataStats `protobuf:"bytes,2,opt,name=member_stats,json=memberStats,proto3" json:"member_stats,omitempty"`
	XXX_NoUnkeyedLiteral struct{}   `json:"-"`
	XXX_unrecognized     []byte     `json:"-"`
	XXX_sizecache        int32      `json:"-"`
}

The data statistics of a series of ARRAY values.

func (*ArrayStats) Descriptor 

func (*ArrayStats) Descriptor() ([]byte, []int)

func (*ArrayStats) GetMemberStats 

func (m *ArrayStats) GetMemberStats() *DataStats

func (*ArrayStats) ProtoMessage 

func (*ArrayStats) ProtoMessage()

func (*ArrayStats) Reset 

func (m *ArrayStats) Reset()

func (*ArrayStats) String 

func (m *ArrayStats) String() string

func (*ArrayStats) XXX_DiscardUnknown 

func (m *ArrayStats) XXX_DiscardUnknown()

func (*ArrayStats) XXX_Marshal 

func (m *ArrayStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ArrayStats) XXX_Merge 

func (m *ArrayStats) XXX_Merge(src proto.Message)

func (*ArrayStats) XXX_Size 

func (m *ArrayStats) XXX_Size() int

func (*ArrayStats) XXX_Unmarshal 

func (m *ArrayStats) XXX_Unmarshal(b []byte) error

type AutoMlClient 

type AutoMlClient interface {
	// Creates a dataset.
	CreateDataset(ctx context.Context, in *CreateDatasetRequest, opts ...grpc.CallOption) (*Dataset, error)
	// Gets a dataset.
	GetDataset(ctx context.Context, in *GetDatasetRequest, opts ...grpc.CallOption) (*Dataset, error)
	// Lists datasets in a project.
	ListDatasets(ctx context.Context, in *ListDatasetsRequest, opts ...grpc.CallOption) (*ListDatasetsResponse, error)
	// Updates a dataset.
	UpdateDataset(ctx context.Context, in *UpdateDatasetRequest, opts ...grpc.CallOption) (*Dataset, error)
	// Deletes a dataset and all of its contents.
	// Returns empty response in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteDataset(ctx context.Context, in *DeleteDatasetRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Imports data into a dataset.
	// For Tables this method can only be called on an empty Dataset.
	//
	// For Tables:
	// *   A
	// [schema_inference_version][google.cloud.automl.v1beta1.InputConfig.params]
	//     parameter must be explicitly set.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ImportData(ctx context.Context, in *ImportDataRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Exports dataset's data to the provided output location.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportData(ctx context.Context, in *ExportDataRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Gets an annotation spec.
	GetAnnotationSpec(ctx context.Context, in *GetAnnotationSpecRequest, opts ...grpc.CallOption) (*AnnotationSpec, error)
	// Gets a table spec.
	GetTableSpec(ctx context.Context, in *GetTableSpecRequest, opts ...grpc.CallOption) (*TableSpec, error)
	// Lists table specs in a dataset.
	ListTableSpecs(ctx context.Context, in *ListTableSpecsRequest, opts ...grpc.CallOption) (*ListTableSpecsResponse, error)
	// Updates a table spec.
	UpdateTableSpec(ctx context.Context, in *UpdateTableSpecRequest, opts ...grpc.CallOption) (*TableSpec, error)
	// Gets a column spec.
	GetColumnSpec(ctx context.Context, in *GetColumnSpecRequest, opts ...grpc.CallOption) (*ColumnSpec, error)
	// Lists column specs in a table spec.
	ListColumnSpecs(ctx context.Context, in *ListColumnSpecsRequest, opts ...grpc.CallOption) (*ListColumnSpecsResponse, error)
	// Updates a column spec.
	UpdateColumnSpec(ctx context.Context, in *UpdateColumnSpecRequest, opts ...grpc.CallOption) (*ColumnSpec, error)
	// Creates a model.
	// Returns a Model in the [response][google.longrunning.Operation.response]
	// field when it completes.
	// When you create a model, several model evaluations are created for it:
	// a global evaluation, and one evaluation for each annotation spec.
	CreateModel(ctx context.Context, in *CreateModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Gets a model.
	GetModel(ctx context.Context, in *GetModelRequest, opts ...grpc.CallOption) (*Model, error)
	// Lists models.
	ListModels(ctx context.Context, in *ListModelsRequest, opts ...grpc.CallOption) (*ListModelsResponse, error)
	// Deletes a model.
	// Returns `google.protobuf.Empty` in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteModel(ctx context.Context, in *DeleteModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Deploys a model. If a model is already deployed, deploying it with the
	// same parameters has no effect. Deploying with different parametrs
	// (as e.g. changing
	//
	// [node_number][google.cloud.automl.v1beta1.ImageObjectDetectionModelDeploymentMetadata.node_number])
	//  will reset the deployment state without pausing the model's availability.
	//
	// Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage
	// deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	DeployModel(ctx context.Context, in *DeployModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Undeploys a model. If the model is not deployed this method has no effect.
	//
	// Only applicable for Text Classification, Image Object Detection and Tables;
	// all other domains manage deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	UndeployModel(ctx context.Context, in *UndeployModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Exports a trained, "export-able", model to a user specified Google Cloud
	// Storage location. A model is considered export-able if and only if it has
	// an export format defined for it in
	//
	// [ModelExportOutputConfig][google.cloud.automl.v1beta1.ModelExportOutputConfig].
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportModel(ctx context.Context, in *ExportModelRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Exports examples on which the model was evaluated (i.e. which were in the
	// TEST set of the dataset the model was created from), together with their
	// ground truth annotations and the annotations created (predicted) by the
	// model.
	// The examples, ground truth and predictions are exported in the state
	// they were at the moment the model was evaluated.
	//
	// This export is available only for 30 days since the model evaluation is
	// created.
	//
	// Currently only available for Tables.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportEvaluatedExamples(ctx context.Context, in *ExportEvaluatedExamplesRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
	// Gets a model evaluation.
	GetModelEvaluation(ctx context.Context, in *GetModelEvaluationRequest, opts ...grpc.CallOption) (*ModelEvaluation, error)
	// Lists model evaluations.
	ListModelEvaluations(ctx context.Context, in *ListModelEvaluationsRequest, opts ...grpc.CallOption) (*ListModelEvaluationsResponse, error)
}

AutoMlClient is the client API for AutoMl service.

For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.

func NewAutoMlClient 

func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient

type AutoMlServer 

type AutoMlServer interface {
	// Creates a dataset.
	CreateDataset(context.Context, *CreateDatasetRequest) (*Dataset, error)
	// Gets a dataset.
	GetDataset(context.Context, *GetDatasetRequest) (*Dataset, error)
	// Lists datasets in a project.
	ListDatasets(context.Context, *ListDatasetsRequest) (*ListDatasetsResponse, error)
	// Updates a dataset.
	UpdateDataset(context.Context, *UpdateDatasetRequest) (*Dataset, error)
	// Deletes a dataset and all of its contents.
	// Returns empty response in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteDataset(context.Context, *DeleteDatasetRequest) (*longrunning.Operation, error)
	// Imports data into a dataset.
	// For Tables this method can only be called on an empty Dataset.
	//
	// For Tables:
	// *   A
	// [schema_inference_version][google.cloud.automl.v1beta1.InputConfig.params]
	//     parameter must be explicitly set.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ImportData(context.Context, *ImportDataRequest) (*longrunning.Operation, error)
	// Exports dataset's data to the provided output location.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportData(context.Context, *ExportDataRequest) (*longrunning.Operation, error)
	// Gets an annotation spec.
	GetAnnotationSpec(context.Context, *GetAnnotationSpecRequest) (*AnnotationSpec, error)
	// Gets a table spec.
	GetTableSpec(context.Context, *GetTableSpecRequest) (*TableSpec, error)
	// Lists table specs in a dataset.
	ListTableSpecs(context.Context, *ListTableSpecsRequest) (*ListTableSpecsResponse, error)
	// Updates a table spec.
	UpdateTableSpec(context.Context, *UpdateTableSpecRequest) (*TableSpec, error)
	// Gets a column spec.
	GetColumnSpec(context.Context, *GetColumnSpecRequest) (*ColumnSpec, error)
	// Lists column specs in a table spec.
	ListColumnSpecs(context.Context, *ListColumnSpecsRequest) (*ListColumnSpecsResponse, error)
	// Updates a column spec.
	UpdateColumnSpec(context.Context, *UpdateColumnSpecRequest) (*ColumnSpec, error)
	// Creates a model.
	// Returns a Model in the [response][google.longrunning.Operation.response]
	// field when it completes.
	// When you create a model, several model evaluations are created for it:
	// a global evaluation, and one evaluation for each annotation spec.
	CreateModel(context.Context, *CreateModelRequest) (*longrunning.Operation, error)
	// Gets a model.
	GetModel(context.Context, *GetModelRequest) (*Model, error)
	// Lists models.
	ListModels(context.Context, *ListModelsRequest) (*ListModelsResponse, error)
	// Deletes a model.
	// Returns `google.protobuf.Empty` in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteModel(context.Context, *DeleteModelRequest) (*longrunning.Operation, error)
	// Deploys a model. If a model is already deployed, deploying it with the
	// same parameters has no effect. Deploying with different parametrs
	// (as e.g. changing
	//
	// [node_number][google.cloud.automl.v1beta1.ImageObjectDetectionModelDeploymentMetadata.node_number])
	//  will reset the deployment state without pausing the model's availability.
	//
	// Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage
	// deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	DeployModel(context.Context, *DeployModelRequest) (*longrunning.Operation, error)
	// Undeploys a model. If the model is not deployed this method has no effect.
	//
	// Only applicable for Text Classification, Image Object Detection and Tables;
	// all other domains manage deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	UndeployModel(context.Context, *UndeployModelRequest) (*longrunning.Operation, error)
	// Exports a trained, "export-able", model to a user specified Google Cloud
	// Storage location. A model is considered export-able if and only if it has
	// an export format defined for it in
	//
	// [ModelExportOutputConfig][google.cloud.automl.v1beta1.ModelExportOutputConfig].
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportModel(context.Context, *ExportModelRequest) (*longrunning.Operation, error)
	// Exports examples on which the model was evaluated (i.e. which were in the
	// TEST set of the dataset the model was created from), together with their
	// ground truth annotations and the annotations created (predicted) by the
	// model.
	// The examples, ground truth and predictions are exported in the state
	// they were at the moment the model was evaluated.
	//
	// This export is available only for 30 days since the model evaluation is
	// created.
	//
	// Currently only available for Tables.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportEvaluatedExamples(context.Context, *ExportEvaluatedExamplesRequest) (*longrunning.Operation, error)
	// Gets a model evaluation.
	GetModelEvaluation(context.Context, *GetModelEvaluationRequest) (*ModelEvaluation, error)
	// Lists model evaluations.
	ListModelEvaluations(context.Context, *ListModelEvaluationsRequest) (*ListModelEvaluationsResponse, error)
}

AutoMlServer is the server API for AutoMl service.

type BatchPredictInputConfig 

type BatchPredictInputConfig struct {
	// Required. The source of the input.
	//
	// Types that are valid to be assigned to Source:
	//	*BatchPredictInputConfig_GcsSource
	//	*BatchPredictInputConfig_BigquerySource
	Source               isBatchPredictInputConfig_Source `protobuf_oneof:"source"`
	XXX_NoUnkeyedLiteral struct{}                         `json:"-"`
	XXX_unrecognized     []byte                           `json:"-"`
	XXX_sizecache        int32                            `json:"-"`
}

Input configuration for BatchPredict Action.

The format of input depends on the ML problem of the model used for prediction. As input source the [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] is expected, unless specified otherwise.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

  • For Image Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png

  • For Image Object Detection: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png

  • For Video Classification: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf

  • For Video Object Tracking: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,240 gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf

  • For Text Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 60,000 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf

  • For Text Sentiment: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 500 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf

  • For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or as documents (for a single BatchPredict call only one of the these formats may be used). The in-line .JSONL file(s) contain per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in json representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique. The document .JSONL file(s) contain, per line, a proto that wraps a Document proto with input_config set. Only PDF documents are supported now, and each document must be up to 2MB large. Any given .JSONL file must be 100MB or smaller, and no more than 20 files may be given. Sample in-line JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n): { "id": "my_first_id", "text_snippet": { "content": "dog car cat"}, "text_features": [ { "text_segment": {"start_offset": 4, "end_offset": 6}, "structural_type": PARAGRAPH, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ] }, } ], }\n { "id": "2", "text_snippet": { "content": "An elaborate content", "mime_type": "text/plain" } } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ] } } } }

  • For Tables: Either [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or

[bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]. 

GCS case:
  CSV file(s), each by itself 10GB or smaller and total size must be
  100GB or smaller, where first file must have a header containing
  column names. If the first row of a subsequent file is the same as
  the header, then it is also treated as a header. All other rows
  contain values for the corresponding columns.
  The column names must contain the model's

[input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] 

(order doesn't matter). The columns corresponding to the model's
input feature column specs must contain values compatible with the
column spec's data types. Prediction on all the rows, i.e. the CSV
lines, will be attempted. For FORECASTING

[prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: 

all columns having

[TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] 

type will be ignored.
First three sample rows of a CSV file:
  "First Name","Last Name","Dob","Addresses"

"John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"

"Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} 

BigQuery case:
  An URI of a BigQuery table. The user data size of the BigQuery
  table must be 100GB or smaller.
  The column names must contain the model's

[input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] 

(order doesn't matter). The columns corresponding to the model's
input feature column specs must contain values compatible with the
column spec's data types. Prediction on all the rows of the table
will be attempted. For FORECASTING

[prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: 

all columns having

[TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] 

         type will be ignored.

Definitions:
GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi".
TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
               double quotes ("")
TIME_SEGMENT_START = TIME_OFFSET
                     Expresses a beginning, inclusive, of a time segment
                     within an
                     example that has a time dimension (e.g. video).
TIME_SEGMENT_END = TIME_OFFSET
                   Expresses an end, exclusive, of a time segment within
                   an example that has a time dimension (e.g. video).
TIME_OFFSET = A number of seconds as measured from the start of an
              example (e.g. video). Fractions are allowed, up to a
              microsecond precision. "inf" is allowed and it means the end
              of the example.

Errors:
If any of the provided CSV files can't be parsed or if more than certain
percent of CSV rows cannot be processed then the operation fails and
prediction does not happen. Regardless of overall success or failure the
per-row failures, up to a certain count cap, will be listed in
Operation.metadata.partial_failures.

func (*BatchPredictInputConfig) Descriptor 

func (*BatchPredictInputConfig) Descriptor() ([]byte, []int)

func (*BatchPredictInputConfig) GetBigquerySource 

func (m *BatchPredictInputConfig) GetBigquerySource() *BigQuerySource

func (*BatchPredictInputConfig) GetGcsSource 

func (m *BatchPredictInputConfig) GetGcsSource() *GcsSource

func (*BatchPredictInputConfig) GetSource 

func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source

func (*BatchPredictInputConfig) ProtoMessage 

func (*BatchPredictInputConfig) ProtoMessage()

func (*BatchPredictInputConfig) Reset 

func (m *BatchPredictInputConfig) Reset()

func (*BatchPredictInputConfig) String 

func (m *BatchPredictInputConfig) String() string

func (*BatchPredictInputConfig) XXX_DiscardUnknown 

func (m *BatchPredictInputConfig) XXX_DiscardUnknown()

func (*BatchPredictInputConfig) XXX_Marshal 

func (m *BatchPredictInputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BatchPredictInputConfig) XXX_Merge 

func (m *BatchPredictInputConfig) XXX_Merge(src proto.Message)

func (*BatchPredictInputConfig) XXX_OneofWrappers 

func (*BatchPredictInputConfig) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*BatchPredictInputConfig) XXX_Size 

func (m *BatchPredictInputConfig) XXX_Size() int

func (*BatchPredictInputConfig) XXX_Unmarshal 

func (m *BatchPredictInputConfig) XXX_Unmarshal(b []byte) error

type BatchPredictInputConfig_BigquerySource 

type BatchPredictInputConfig_BigquerySource struct {
	BigquerySource *BigQuerySource `protobuf:"bytes,2,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}

type BatchPredictInputConfig_GcsSource 

type BatchPredictInputConfig_GcsSource struct {
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

type BatchPredictOperationMetadata 

type BatchPredictOperationMetadata struct {
	// Output only. The input config that was given upon starting this
	// batch predict operation.
	InputConfig *BatchPredictInputConfig `protobuf:"bytes,1,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// Output only. Information further describing this batch predict's output.
	OutputInfo           *BatchPredictOperationMetadata_BatchPredictOutputInfo `protobuf:"bytes,2,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                                              `json:"-"`
	XXX_unrecognized     []byte                                                `json:"-"`
	XXX_sizecache        int32                                                 `json:"-"`
}

Details of BatchPredict operation.

func (*BatchPredictOperationMetadata) Descriptor 

func (*BatchPredictOperationMetadata) Descriptor() ([]byte, []int)

func (*BatchPredictOperationMetadata) GetInputConfig 

func (m *BatchPredictOperationMetadata) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictOperationMetadata) GetOutputInfo 

func (m *BatchPredictOperationMetadata) GetOutputInfo() *BatchPredictOperationMetadata_BatchPredictOutputInfo

func (*BatchPredictOperationMetadata) ProtoMessage 

func (*BatchPredictOperationMetadata) ProtoMessage()

func (*BatchPredictOperationMetadata) Reset 

func (m *BatchPredictOperationMetadata) Reset()

func (*BatchPredictOperationMetadata) String 

func (m *BatchPredictOperationMetadata) String() string

func (*BatchPredictOperationMetadata) XXX_DiscardUnknown 

func (m *BatchPredictOperationMetadata) XXX_DiscardUnknown()

func (*BatchPredictOperationMetadata) XXX_Marshal 

func (m *BatchPredictOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BatchPredictOperationMetadata) XXX_Merge 

func (m *BatchPredictOperationMetadata) XXX_Merge(src proto.Message)

func (*BatchPredictOperationMetadata) XXX_Size 

func (m *BatchPredictOperationMetadata) XXX_Size() int

func (*BatchPredictOperationMetadata) XXX_Unmarshal 

func (m *BatchPredictOperationMetadata) XXX_Unmarshal(b []byte) error

type BatchPredictOperationMetadata_BatchPredictOutputInfo 

type BatchPredictOperationMetadata_BatchPredictOutputInfo struct {
	// The output location into which prediction output is written.
	//
	// Types that are valid to be assigned to OutputLocation:
	//	*BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory
	//	*BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset
	OutputLocation       isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation `protobuf_oneof:"output_location"`
	XXX_NoUnkeyedLiteral struct{}                                                              `json:"-"`
	XXX_unrecognized     []byte                                                                `json:"-"`
	XXX_sizecache        int32                                                                 `json:"-"`
}

Further describes this batch predict's output. Supplements

BatchPredictOutputConfig[google.cloud.automl.v1beta1.BatchPredictOutputConfig].

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Descriptor 

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Descriptor() ([]byte, []int)

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetBigqueryOutputDataset 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetBigqueryOutputDataset() string

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory() string

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation() isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage 

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) String 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) String() string

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_DiscardUnknown 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_DiscardUnknown()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_Marshal 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_Merge 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_Merge(src proto.Message)

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_OneofWrappers 

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_Size 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_Size() int

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_Unmarshal 

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) XXX_Unmarshal(b []byte) error

type BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset 

type BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset struct {
	BigqueryOutputDataset string `protobuf:"bytes,2,opt,name=bigquery_output_dataset,json=bigqueryOutputDataset,proto3,oneof"`
}

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory 

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory struct {
	GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3,oneof"`
}

type BatchPredictOutputConfig 

type BatchPredictOutputConfig struct {
	// Required. The destination of the output.
	//
	// Types that are valid to be assigned to Destination:
	//	*BatchPredictOutputConfig_GcsDestination
	//	*BatchPredictOutputConfig_BigqueryDestination
	Destination          isBatchPredictOutputConfig_Destination `protobuf_oneof:"destination"`
	XXX_NoUnkeyedLiteral struct{}                               `json:"-"`
	XXX_unrecognized     []byte                                 `json:"-"`
	XXX_sizecache        int32                                  `json:"-"`
}

Output configuration for BatchPredict Action.

As destination the

[gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination] must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for.

  • For Image Classification: In the created directory files `image_classification_1.jsonl`, `image_classification_2.jsonl`,...,`image_classification_N.jsonl` will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 

       containing only `code` and `message`fields.

*  For Image Object Detection:
       In the created directory files `image_object_detection_1.jsonl`,
       `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl`
       will be created, where N may be 1, and depends on the
       total number of the successfully predicted images and annotations.
       Each .JSONL file will contain, per line, a JSON representation of a
       proto that wraps image's "ID" : "<id_value>" followed by a list of
       zero or more AnnotationPayload protos (called annotations), which
       have image_object_detection detail populated. A single image will
       be listed only once with all its annotations, and its annotations
       will never be split across files.
       If prediction for any image failed (partially or completely), then
       additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
       files will be created (N depends on total number of failed
       predictions). These files will have a JSON representation of a proto
       that wraps the same "ID" : "<id_value>" but here followed by
       exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 

       containing only `code` and `message`fields.
*  For Video Classification:
       In the created directory a video_classification.csv file, and a .JSON
       file per each video classification requested in the input (i.e. each
       line in given CSV(s)), will be created.

       The format of video_classification.csv is:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS 

       where:
       GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
           the prediction input lines (i.e. video_classification.csv has
           precisely the same number of lines as the prediction input had.)
       JSON_FILE_NAME = Name of .JSON file in the output directory, which
           contains prediction responses for the video time segment.
       STATUS = "OK" if prediction completed successfully, or an error code
           with message otherwise. If STATUS is not "OK" then the .JSON file
           for that line may not exist or be empty.

       Each .JSON file, assuming STATUS is "OK", will contain a list of
       AnnotationPayload protos in JSON format, which are the predictions
       for the video time segment the file is assigned to in the
       video_classification.csv. All AnnotationPayload protos will have
       video_classification field set, and will be sorted by
       video_classification.type field (note that the returned types are
       governed by `classifaction_types` parameter in
       [PredictService.BatchPredictRequest.params][]).

*  For Video Object Tracking:
       In the created directory a video_object_tracking.csv file will be
       created, and multiple files video_object_trackinng_1.json,
       video_object_trackinng_2.json,..., video_object_trackinng_N.json,
       where N is the number of requests in the input (i.e. the number of
       lines in given CSV(s)).

       The format of video_object_tracking.csv is:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS 

       where:
       GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
           the prediction input lines (i.e. video_object_tracking.csv has
           precisely the same number of lines as the prediction input had.)
       JSON_FILE_NAME = Name of .JSON file in the output directory, which
           contains prediction responses for the video time segment.
       STATUS = "OK" if prediction completed successfully, or an error
           code with message otherwise. If STATUS is not "OK" then the .JSON
           file for that line may not exist or be empty.

       Each .JSON file, assuming STATUS is "OK", will contain a list of
       AnnotationPayload protos in JSON format, which are the predictions
       for each frame of the video time segment the file is assigned to in
       video_object_tracking.csv. All AnnotationPayload protos will have
       video_object_tracking field set.
*  For Text Classification:
       In the created directory files `text_classification_1.jsonl`,
       `text_classification_2.jsonl`,...,`text_classification_N.jsonl`
       will be created, where N may be 1, and depends on the
       total number of inputs and annotations found.

       Each .JSONL file will contain, per line, a JSON representation of a
       proto that wraps input text snippet or input text file and a list of
       zero or more AnnotationPayload protos (called annotations), which
       have classification detail populated. A single text snippet or file
       will be listed only once with all its annotations, and its
       annotations will never be split across files.

       If prediction for any text snippet or file failed (partially or
       completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
       `errors_N.jsonl` files will be created (N depends on total number of
       failed predictions). These files will have a JSON representation of a
       proto that wraps input text snippet or input text file followed by
       exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 

       containing only `code` and `message`.

*  For Text Sentiment:
       In the created directory files `text_sentiment_1.jsonl`,
       `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl`
       will be created, where N may be 1, and depends on the
       total number of inputs and annotations found.

       Each .JSONL file will contain, per line, a JSON representation of a
       proto that wraps input text snippet or input text file and a list of
       zero or more AnnotationPayload protos (called annotations), which
       have text_sentiment detail populated. A single text snippet or file
       will be listed only once with all its annotations, and its
       annotations will never be split across files.

       If prediction for any text snippet or file failed (partially or
       completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
       `errors_N.jsonl` files will be created (N depends on total number of
       failed predictions). These files will have a JSON representation of a
       proto that wraps input text snippet or input text file followed by
       exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 

      containing only `code` and `message`.

*  For Text Extraction:
      In the created directory files `text_extraction_1.jsonl`,
      `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl`
      will be created, where N may be 1, and depends on the
      total number of inputs and annotations found.
      The contents of these .JSONL file(s) depend on whether the input
      used inline text, or documents.
      If input was inline, then each .JSONL file will contain, per line,
        a JSON representation of a proto that wraps given in request text
        snippet's "id" (if specified), followed by input text snippet,
        and a list of zero or more
        AnnotationPayload protos (called annotations), which have
        text_extraction detail populated. A single text snippet will be
        listed only once with all its annotations, and its annotations will
        never be split across files.
      If input used documents, then each .JSONL file will contain, per
        line, a JSON representation of a proto that wraps given in request
        document proto, followed by its OCR-ed representation in the form
        of a text snippet, finally followed by a list of zero or more
        AnnotationPayload protos (called annotations), which have
        text_extraction detail populated and refer, via their indices, to
        the OCR-ed text snippet. A single document (and its text snippet)
        will be listed only once with all its annotations, and its
        annotations will never be split across files.
      If prediction for any text snippet failed (partially or completely),
      then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
      `errors_N.jsonl` files will be created (N depends on total number of
      failed predictions). These files will have a JSON representation of a
      proto that wraps either the "id" : "<id_value>" (in case of inline)
      or the document proto (in case of document) but here followed by
      exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 

       containing only `code` and `message`.

*  For Tables:
       Output depends on whether

[gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination] 

or

[bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination] 

is set (either is allowed).
GCS case:
  In the created directory files `tables_1.csv`, `tables_2.csv`,...,
  `tables_N.csv` will be created, where N may be 1, and depends on
  the total number of the successfully predicted rows.
  For all CLASSIFICATION

[prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: 

Each .csv file will contain a header, listing all columns'

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] 

given on input followed by M target column names in the format of

"<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>_<target 

  value>_score" where M is the number of distinct target values,
  i.e. number of distinct values in the target column of the table
  used to train the model. Subsequent lines will contain the
  respective values of successfully predicted rows, with the last,
  i.e. the target, columns having the corresponding prediction
  [scores][google.cloud.automl.v1beta1.TablesAnnotation.score].
For REGRESSION and FORECASTING

[prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: 

Each .csv file will contain a header, listing all columns'
[display_name-s][google.cloud.automl.v1beta1.display_name] given
on input followed by the predicted target column with name in the
format of

"predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>" 

Subsequent lines will contain the respective values of
successfully predicted rows, with the last, i.e. the target,
column having the predicted target value.
If prediction for any rows failed, then an additional
`errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be
created (N depends on total number of failed rows). These files
will have analogous format as `tables_*.csv`, but always with a
single target column having

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 

    represented as a JSON string, and containing only `code` and
    `message`.
BigQuery case:

[bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination] 

pointing to a BigQuery project must be set. In the given project a
new dataset will be created with name
`prediction_<model-display-name>_<timestamp-of-prediction-call>`
where <model-display-name> will be made
BigQuery-dataset-name compatible (e.g. most special characters will
become underscores), and timestamp will be in
YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset
two tables will be created, `predictions`, and `errors`.
The `predictions` table's column names will be the input columns'

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] 

followed by the target column with name in the format of

"predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>" 

The input feature columns will contain the respective values of
successfully predicted rows, with the target column having an
ARRAY of

[AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload], 

represented as STRUCT-s, containing
[TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].
The `errors` table contains rows for which the prediction has
failed, it has analogous input columns while the target column name
is in the format of

"errors_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>", 

and as a value has

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 

represented as a STRUCT, and containing only `code` and `message`.

func (*BatchPredictOutputConfig) Descriptor 

func (*BatchPredictOutputConfig) Descriptor() ([]byte, []int)

func (*BatchPredictOutputConfig) GetBigqueryDestination 

func (m *BatchPredictOutputConfig) GetBigqueryDestination() *BigQueryDestination

func (*BatchPredictOutputConfig) GetDestination 

func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination

func (*BatchPredictOutputConfig) GetGcsDestination 

func (m *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination

func (*BatchPredictOutputConfig) ProtoMessage 

func (*BatchPredictOutputConfig) ProtoMessage()

func (*BatchPredictOutputConfig) Reset 

func (m *BatchPredictOutputConfig) Reset()

func (*BatchPredictOutputConfig) String 

func (m *BatchPredictOutputConfig) String() string

func (*BatchPredictOutputConfig) XXX_DiscardUnknown 

func (m *BatchPredictOutputConfig) XXX_DiscardUnknown()

func (*BatchPredictOutputConfig) XXX_Marshal 

func (m *BatchPredictOutputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BatchPredictOutputConfig) XXX_Merge 

func (m *BatchPredictOutputConfig) XXX_Merge(src proto.Message)

func (*BatchPredictOutputConfig) XXX_OneofWrappers 

func (*BatchPredictOutputConfig) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*BatchPredictOutputConfig) XXX_Size 

func (m *BatchPredictOutputConfig) XXX_Size() int

func (*BatchPredictOutputConfig) XXX_Unmarshal 

func (m *BatchPredictOutputConfig) XXX_Unmarshal(b []byte) error

type BatchPredictOutputConfig_BigqueryDestination 

type BatchPredictOutputConfig_BigqueryDestination struct {
	BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}

type BatchPredictOutputConfig_GcsDestination 

type BatchPredictOutputConfig_GcsDestination struct {
	GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

type BatchPredictRequest 

type BatchPredictRequest struct {
	// Required. Name of the model requested to serve the batch prediction.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The input configuration for batch prediction.
	InputConfig *BatchPredictInputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// Required. The Configuration specifying where output predictions should
	// be written.
	OutputConfig *BatchPredictOutputConfig `protobuf:"bytes,4,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	// Required. Additional domain-specific parameters for the predictions, any string must
	// be up to 25000 characters long.
	//
	// *  For Text Classification:
	//
	//    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
	//         makes predictions for a text snippet, it will only produce results
	//         that have at least this confidence score. The default is 0.5.
	//
	// *  For Image Classification:
	//
	//    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
	//         makes predictions for an image, it will only produce results that
	//         have at least this confidence score. The default is 0.5.
	//
	// *  For Image Object Detection:
	//
	//    `score_threshold` - (float) When Model detects objects on the image,
	//        it will only produce bounding boxes which have at least this
	//        confidence score. Value in 0 to 1 range, default is 0.5.
	//    `max_bounding_box_count` - (int64) No more than this number of bounding
	//        boxes will be produced per image. Default is 100, the
	//        requested value may be limited by server.
	//
	// *  For Video Classification :
	//
	//    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
	//        makes predictions for a video, it will only produce results that
	//        have at least this confidence score. The default is 0.5.
	//    `segment_classification` - (boolean) Set to true to request
	//        segment-level classification. AutoML Video Intelligence returns
	//        labels and their confidence scores for the entire segment of the
	//        video that user specified in the request configuration.
	//        The default is "true".
	//    `shot_classification` - (boolean) Set to true to request shot-level
	//        classification. AutoML Video Intelligence determines the boundaries
	//        for each camera shot in the entire segment of the video that user
	//        specified in the request configuration. AutoML Video Intelligence
	//        then returns labels and their confidence scores for each detected
	//        shot, along with the start and end time of the shot.
	//        WARNING: Model evaluation is not done for this classification type,
	//        the quality of it depends on training data, but there are no metrics
	//        provided to describe that quality. The default is "false".
	//    `1s_interval_classification` - (boolean) Set to true to request
	//        classification for a video at one-second intervals. AutoML Video
	//        Intelligence returns labels and their confidence scores for each
	//        second of the entire segment of the video that user specified in the
	//        request configuration.
	//        WARNING: Model evaluation is not done for this classification
	//        type, the quality of it depends on training data, but there are no
	//        metrics provided to describe that quality. The default is
	//        "false".
	//
	// *  For Tables:
	//
	//    feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
	//        should be populated in the returned TablesAnnotations. The
	//        default is false.
	//
	// *  For Video Object Tracking:
	//
	//    `score_threshold` - (float) When Model detects objects on video frames,
	//        it will only produce bounding boxes which have at least this
	//        confidence score. Value in 0 to 1 range, default is 0.5.
	//    `max_bounding_box_count` - (int64) No more than this number of bounding
	//        boxes will be returned per frame. Default is 100, the requested
	//        value may be limited by server.
	//    `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
	//      at least that long as a relative value of video frame size will be
	//      returned. Value in 0 to 1 range. Default is 0.
	Params               map[string]string `` /* 153-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}          `json:"-"`
	XXX_unrecognized     []byte            `json:"-"`
	XXX_sizecache        int32             `json:"-"`
}

Request message for [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict].

func (*BatchPredictRequest) Descriptor 

func (*BatchPredictRequest) Descriptor() ([]byte, []int)

func (*BatchPredictRequest) GetInputConfig 

func (m *BatchPredictRequest) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictRequest) GetName 

func (m *BatchPredictRequest) GetName() string

func (*BatchPredictRequest) GetOutputConfig 

func (m *BatchPredictRequest) GetOutputConfig() *BatchPredictOutputConfig

func (*BatchPredictRequest) GetParams 

func (m *BatchPredictRequest) GetParams() map[string]string

func (*BatchPredictRequest) ProtoMessage 

func (*BatchPredictRequest) ProtoMessage()

func (*BatchPredictRequest) Reset 

func (m *BatchPredictRequest) Reset()

func (*BatchPredictRequest) String 

func (m *BatchPredictRequest) String() string

func (*BatchPredictRequest) XXX_DiscardUnknown 

func (m *BatchPredictRequest) XXX_DiscardUnknown()

func (*BatchPredictRequest) XXX_Marshal 

func (m *BatchPredictRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BatchPredictRequest) XXX_Merge 

func (m *BatchPredictRequest) XXX_Merge(src proto.Message)

func (*BatchPredictRequest) XXX_Size 

func (m *BatchPredictRequest) XXX_Size() int

func (*BatchPredictRequest) XXX_Unmarshal 

func (m *BatchPredictRequest) XXX_Unmarshal(b []byte) error

type BatchPredictResult 

type BatchPredictResult struct {
	// Additional domain-specific prediction response metadata.
	//
	// *  For Image Object Detection:
	//  `max_bounding_box_count` - (int64) At most that many bounding boxes per
	//      image could have been returned.
	//
	// *  For Video Object Tracking:
	//  `max_bounding_box_count` - (int64) At most that many bounding boxes per
	//      frame could have been returned.
	Metadata             map[string]string `` /* 157-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}          `json:"-"`
	XXX_unrecognized     []byte            `json:"-"`
	XXX_sizecache        int32             `json:"-"`
}

Result of the Batch Predict. This message is returned in [response][google.longrunning.Operation.response] of the operation returned by the [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict].

func (*BatchPredictResult) Descriptor 

func (*BatchPredictResult) Descriptor() ([]byte, []int)

func (*BatchPredictResult) GetMetadata 

func (m *BatchPredictResult) GetMetadata() map[string]string

func (*BatchPredictResult) ProtoMessage 

func (*BatchPredictResult) ProtoMessage()

func (*BatchPredictResult) Reset 

func (m *BatchPredictResult) Reset()

func (*BatchPredictResult) String 

func (m *BatchPredictResult) String() string

func (*BatchPredictResult) XXX_DiscardUnknown 

func (m *BatchPredictResult) XXX_DiscardUnknown()

func (*BatchPredictResult) XXX_Marshal 

func (m *BatchPredictResult) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BatchPredictResult) XXX_Merge 

func (m *BatchPredictResult) XXX_Merge(src proto.Message)

func (*BatchPredictResult) XXX_Size 

func (m *BatchPredictResult) XXX_Size() int

func (*BatchPredictResult) XXX_Unmarshal 

func (m *BatchPredictResult) XXX_Unmarshal(b []byte) error

type BigQueryDestination 

type BigQueryDestination struct {
	// Required. BigQuery URI to a project, up to 2000 characters long.
	// Accepted forms:
	// *  BigQuery path e.g. bq://projectId
	OutputUri            string   `protobuf:"bytes,1,opt,name=output_uri,json=outputUri,proto3" json:"output_uri,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

The BigQuery location for the output content.

func (*BigQueryDestination) Descriptor 

func (*BigQueryDestination) Descriptor() ([]byte, []int)

func (*BigQueryDestination) GetOutputUri 

func (m *BigQueryDestination) GetOutputUri() string

func (*BigQueryDestination) ProtoMessage 

func (*BigQueryDestination) ProtoMessage()

func (*BigQueryDestination) Reset 

func (m *BigQueryDestination) Reset()

func (*BigQueryDestination) String 

func (m *BigQueryDestination) String() string

func (*BigQueryDestination) XXX_DiscardUnknown 

func (m *BigQueryDestination) XXX_DiscardUnknown()

func (*BigQueryDestination) XXX_Marshal 

func (m *BigQueryDestination) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BigQueryDestination) XXX_Merge 

func (m *BigQueryDestination) XXX_Merge(src proto.Message)

func (*BigQueryDestination) XXX_Size 

func (m *BigQueryDestination) XXX_Size() int

func (*BigQueryDestination) XXX_Unmarshal 

func (m *BigQueryDestination) XXX_Unmarshal(b []byte) error

type BigQuerySource 

type BigQuerySource struct {
	// Required. BigQuery URI to a table, up to 2000 characters long.
	// Accepted forms:
	// *  BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId
	InputUri             string   `protobuf:"bytes,1,opt,name=input_uri,json=inputUri,proto3" json:"input_uri,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

The BigQuery location for the input content.

func (*BigQuerySource) Descriptor 

func (*BigQuerySource) Descriptor() ([]byte, []int)

func (*BigQuerySource) GetInputUri 

func (m *BigQuerySource) GetInputUri() string

func (*BigQuerySource) ProtoMessage 

func (*BigQuerySource) ProtoMessage()

func (*BigQuerySource) Reset 

func (m *BigQuerySource) Reset()

func (*BigQuerySource) String 

func (m *BigQuerySource) String() string

func (*BigQuerySource) XXX_DiscardUnknown 

func (m *BigQuerySource) XXX_DiscardUnknown()

func (*BigQuerySource) XXX_Marshal 

func (m *BigQuerySource) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BigQuerySource) XXX_Merge 

func (m *BigQuerySource) XXX_Merge(src proto.Message)

func (*BigQuerySource) XXX_Size 

func (m *BigQuerySource) XXX_Size() int

func (*BigQuerySource) XXX_Unmarshal 

func (m *BigQuerySource) XXX_Unmarshal(b []byte) error

type BoundingBoxMetricsEntry 

type BoundingBoxMetricsEntry struct {
	// Output only. The intersection-over-union threshold value used to compute
	// this metrics entry.
	IouThreshold float32 `protobuf:"fixed32,1,opt,name=iou_threshold,json=iouThreshold,proto3" json:"iou_threshold,omitempty"`
	// Output only. The mean average precision, most often close to au_prc.
	MeanAveragePrecision float32 `protobuf:"fixed32,2,opt,name=mean_average_precision,json=meanAveragePrecision,proto3" json:"mean_average_precision,omitempty"`
	// Output only. Metrics for each label-match confidence_threshold from
	// 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is
	// derived from them.
	ConfidenceMetricsEntries []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry `` /* 135-byte string literal not displayed */
	XXX_NoUnkeyedLiteral     struct{}                                          `json:"-"`
	XXX_unrecognized         []byte                                            `json:"-"`
	XXX_sizecache            int32                                             `json:"-"`
}

Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.

func (*BoundingBoxMetricsEntry) Descriptor 

func (*BoundingBoxMetricsEntry) Descriptor() ([]byte, []int)

func (*BoundingBoxMetricsEntry) GetConfidenceMetricsEntries 

func (m *BoundingBoxMetricsEntry) GetConfidenceMetricsEntries() []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry

func (*BoundingBoxMetricsEntry) GetIouThreshold 

func (m *BoundingBoxMetricsEntry) GetIouThreshold() float32

func (*BoundingBoxMetricsEntry) GetMeanAveragePrecision 

func (m *BoundingBoxMetricsEntry) GetMeanAveragePrecision() float32

func (*BoundingBoxMetricsEntry) ProtoMessage 

func (*BoundingBoxMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry) Reset 

func (m *BoundingBoxMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry) String 

func (m *BoundingBoxMetricsEntry) String() string

func (*BoundingBoxMetricsEntry) XXX_DiscardUnknown 

func (m *BoundingBoxMetricsEntry) XXX_DiscardUnknown()

func (*BoundingBoxMetricsEntry) XXX_Marshal 

func (m *BoundingBoxMetricsEntry) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BoundingBoxMetricsEntry) XXX_Merge 

func (m *BoundingBoxMetricsEntry) XXX_Merge(src proto.Message)

func (*BoundingBoxMetricsEntry) XXX_Size 

func (m *BoundingBoxMetricsEntry) XXX_Size() int

func (*BoundingBoxMetricsEntry) XXX_Unmarshal 

func (m *BoundingBoxMetricsEntry) XXX_Unmarshal(b []byte) error

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry 

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry struct {
	// Output only. The confidence threshold value used to compute the metrics.
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
	// Output only. Recall under the given confidence threshold.
	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. Precision under the given confidence threshold.
	Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score              float32  `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Metrics for a single confidence threshold.

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Descriptor 

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage 

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String() string

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_DiscardUnknown 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_DiscardUnknown()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_Marshal 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_Merge 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_Merge(src proto.Message)

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_Size 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_Size() int

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_Unmarshal 

func (m *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) XXX_Unmarshal(b []byte) error

type BoundingPoly 

type BoundingPoly struct {
	// Output only . The bounding polygon normalized vertices.
	NormalizedVertices   []*NormalizedVertex `protobuf:"bytes,2,rep,name=normalized_vertices,json=normalizedVertices,proto3" json:"normalized_vertices,omitempty"`
	XXX_NoUnkeyedLiteral struct{}            `json:"-"`
	XXX_unrecognized     []byte              `json:"-"`
	XXX_sizecache        int32               `json:"-"`
}

A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.

func (*BoundingPoly) Descriptor 

func (*BoundingPoly) Descriptor() ([]byte, []int)

func (*BoundingPoly) GetNormalizedVertices 

func (m *BoundingPoly) GetNormalizedVertices() []*NormalizedVertex

func (*BoundingPoly) ProtoMessage 

func (*BoundingPoly) ProtoMessage()

func (*BoundingPoly) Reset 

func (m *BoundingPoly) Reset()

func (*BoundingPoly) String 

func (m *BoundingPoly) String() string

func (*BoundingPoly) XXX_DiscardUnknown 

func (m *BoundingPoly) XXX_DiscardUnknown()

func (*BoundingPoly) XXX_Marshal 

func (m *BoundingPoly) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*BoundingPoly) XXX_Merge 

func (m *BoundingPoly) XXX_Merge(src proto.Message)

func (*BoundingPoly) XXX_Size 

func (m *BoundingPoly) XXX_Size() int

func (*BoundingPoly) XXX_Unmarshal 

func (m *BoundingPoly) XXX_Unmarshal(b []byte) error

type CategoryStats 

type CategoryStats struct {
	// The statistics of the top 20 CATEGORY values, ordered by
	//
	// [count][google.cloud.automl.v1beta1.CategoryStats.SingleCategoryStats.count].
	TopCategoryStats     []*CategoryStats_SingleCategoryStats `protobuf:"bytes,1,rep,name=top_category_stats,json=topCategoryStats,proto3" json:"top_category_stats,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                             `json:"-"`
	XXX_unrecognized     []byte                               `json:"-"`
	XXX_sizecache        int32                                `json:"-"`
}

The data statistics of a series of CATEGORY values.

func (*CategoryStats) Descriptor 

func (*CategoryStats) Descriptor() ([]byte, []int)

func (*CategoryStats) GetTopCategoryStats 

func (m *CategoryStats) GetTopCategoryStats() []*CategoryStats_SingleCategoryStats

func (*CategoryStats) ProtoMessage 

func (*CategoryStats) ProtoMessage()

func (*CategoryStats) Reset 

func (m *CategoryStats) Reset()

func (*CategoryStats) String 

func (m *CategoryStats) String() string

func (*CategoryStats) XXX_DiscardUnknown 

func (m *CategoryStats) XXX_DiscardUnknown()

func (*CategoryStats) XXX_Marshal 

func (m *CategoryStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*CategoryStats) XXX_Merge 

func (m *CategoryStats) XXX_Merge(src proto.Message)

func (*CategoryStats) XXX_Size 

func (m *CategoryStats) XXX_Size() int

func (*CategoryStats) XXX_Unmarshal 

func (m *CategoryStats) XXX_Unmarshal(b []byte) error

type CategoryStats_SingleCategoryStats 

type CategoryStats_SingleCategoryStats struct {
	// The CATEGORY value.
	Value string `protobuf:"bytes,1,opt,name=value,proto3" json:"value,omitempty"`
	// The number of occurrences of this value in the series.
	Count                int64    `protobuf:"varint,2,opt,name=count,proto3" json:"count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

The statistics of a single CATEGORY value.

func (*CategoryStats_SingleCategoryStats) Descriptor 

func (*CategoryStats_SingleCategoryStats) Descriptor() ([]byte, []int)

func (*CategoryStats_SingleCategoryStats) GetCount 

func (m *CategoryStats_SingleCategoryStats) GetCount() int64

func (*CategoryStats_SingleCategoryStats) GetValue 

func (m *CategoryStats_SingleCategoryStats) GetValue() string

func (*CategoryStats_SingleCategoryStats) ProtoMessage 

func (*CategoryStats_SingleCategoryStats) ProtoMessage()

func (*CategoryStats_SingleCategoryStats) Reset 

func (m *CategoryStats_SingleCategoryStats) Reset()

func (*CategoryStats_SingleCategoryStats) String 

func (m *CategoryStats_SingleCategoryStats) String() string

func (*CategoryStats_SingleCategoryStats) XXX_DiscardUnknown 

func (m *CategoryStats_SingleCategoryStats) XXX_DiscardUnknown()

func (*CategoryStats_SingleCategoryStats) XXX_Marshal 

func (m *CategoryStats_SingleCategoryStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*CategoryStats_SingleCategoryStats) XXX_Merge 

func (m *CategoryStats_SingleCategoryStats) XXX_Merge(src proto.Message)

func (*CategoryStats_SingleCategoryStats) XXX_Size 

func (m *CategoryStats_SingleCategoryStats) XXX_Size() int

func (*CategoryStats_SingleCategoryStats) XXX_Unmarshal 

func (m *CategoryStats_SingleCategoryStats) XXX_Unmarshal(b []byte) error

type ClassificationAnnotation 

type ClassificationAnnotation struct {
	// Output only. A confidence estimate between 0.0 and 1.0. A higher value
	// means greater confidence that the annotation is positive. If a user
	// approves an annotation as negative or positive, the score value remains
	// unchanged. If a user creates an annotation, the score is 0 for negative or
	// 1 for positive.
	Score                float32  `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Contains annotation details specific to classification.

func (*ClassificationAnnotation) Descriptor 

func (*ClassificationAnnotation) Descriptor() ([]byte, []int)

func (*ClassificationAnnotation) GetScore 

func (m *ClassificationAnnotation) GetScore() float32

func (*ClassificationAnnotation) ProtoMessage 

func (*ClassificationAnnotation) ProtoMessage()

func (*ClassificationAnnotation) Reset 

func (m *ClassificationAnnotation) Reset()

func (*ClassificationAnnotation) String 

func (m *ClassificationAnnotation) String() string

func (*ClassificationAnnotation) XXX_DiscardUnknown 

func (m *ClassificationAnnotation) XXX_DiscardUnknown()

func (*ClassificationAnnotation) XXX_Marshal 

func (m *ClassificationAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ClassificationAnnotation) XXX_Merge 

func (m *ClassificationAnnotation) XXX_Merge(src proto.Message)

func (*ClassificationAnnotation) XXX_Size 

func (m *ClassificationAnnotation) XXX_Size() int

func (*ClassificationAnnotation) XXX_Unmarshal 

func (m *ClassificationAnnotation) XXX_Unmarshal(b []byte) error

type ClassificationEvaluationMetrics 

type ClassificationEvaluationMetrics struct {
	// Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
	// for the overall evaluation.
	AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`
	// Output only. The Area Under Precision-Recall Curve metric based on priors.
	// Micro-averaged for the overall evaluation.
	// Deprecated.
	BaseAuPrc float32 `protobuf:"fixed32,2,opt,name=base_au_prc,json=baseAuPrc,proto3" json:"base_au_prc,omitempty"` // Deprecated: Do not use.
	// Output only. The Area Under Receiver Operating Characteristic curve metric.
	// Micro-averaged for the overall evaluation.
	AuRoc float32 `protobuf:"fixed32,6,opt,name=au_roc,json=auRoc,proto3" json:"au_roc,omitempty"`
	// Output only. The Log Loss metric.
	LogLoss float32 `protobuf:"fixed32,7,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"`
	// Output only. Metrics for each confidence_threshold in
	// 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and
	// position_threshold = INT32_MAX_VALUE.
	// ROC and precision-recall curves, and other aggregated metrics are derived
	// from them. The confidence metrics entries may also be supplied for
	// additional values of position_threshold, but from these no aggregated
	// metrics are computed.
	ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry `` /* 129-byte string literal not displayed */
	// Output only. Confusion matrix of the evaluation.
	// Only set for MULTICLASS classification problems where number
	// of labels is no more than 10.
	// Only set for model level evaluation, not for evaluation per label.
	ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`
	// Output only. The annotation spec ids used for this evaluation.
	AnnotationSpecId     []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.

func (*ClassificationEvaluationMetrics) Descriptor 

func (*ClassificationEvaluationMetrics) Descriptor() ([]byte, []int)

func (*ClassificationEvaluationMetrics) GetAnnotationSpecId 

func (m *ClassificationEvaluationMetrics) GetAnnotationSpecId() []string

func (*ClassificationEvaluationMetrics) GetAuPrc 

func (m *ClassificationEvaluationMetrics) GetAuPrc() float32

func (*ClassificationEvaluationMetrics) GetAuRoc 

func (m *ClassificationEvaluationMetrics) GetAuRoc() float32

func (*ClassificationEvaluationMetrics) GetBaseAuPrc deprecated 

func (m *ClassificationEvaluationMetrics) GetBaseAuPrc() float32

Deprecated: Do not use.

func (*ClassificationEvaluationMetrics) GetConfidenceMetricsEntry 

func (m *ClassificationEvaluationMetrics) GetConfidenceMetricsEntry() []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry

func (*ClassificationEvaluationMetrics) GetConfusionMatrix 

func (m *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix

func (*ClassificationEvaluationMetrics) GetLogLoss 

func (m *ClassificationEvaluationMetrics) GetLogLoss() float32

func (*ClassificationEvaluationMetrics) ProtoMessage 

func (*ClassificationEvaluationMetrics) ProtoMessage()

func (*ClassificationEvaluationMetrics) Reset 

func (m *ClassificationEvaluationMetrics) Reset()

func (*ClassificationEvaluationMetrics) String 

func (m *ClassificationEvaluationMetrics) String() string

func (*ClassificationEvaluationMetrics) XXX_DiscardUnknown 

func (m *ClassificationEvaluationMetrics) XXX_DiscardUnknown()

func (*ClassificationEvaluationMetrics) XXX_Marshal 

func (m *ClassificationEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ClassificationEvaluationMetrics) XXX_Merge 

func (m *ClassificationEvaluationMetrics) XXX_Merge(src proto.Message)

func (*ClassificationEvaluationMetrics) XXX_Size 

func (m *ClassificationEvaluationMetrics) XXX_Size() int

func (*ClassificationEvaluationMetrics) XXX_Unmarshal 

func (m *ClassificationEvaluationMetrics) XXX_Unmarshal(b []byte) error

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry 

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct {
	// Output only. Metrics are computed with an assumption that the model
	// never returns predictions with score lower than this value.
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
	// Output only. Metrics are computed with an assumption that the model
	// always returns at most this many predictions (ordered by their score,
	// descendingly), but they all still need to meet the confidence_threshold.
	PositionThreshold int32 `protobuf:"varint,14,opt,name=position_threshold,json=positionThreshold,proto3" json:"position_threshold,omitempty"`
	// Output only. Recall (True Positive Rate) for the given confidence
	// threshold.
	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. Precision for the given confidence threshold.
	Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. False Positive Rate for the given confidence threshold.
	FalsePositiveRate float32 `protobuf:"fixed32,8,opt,name=false_positive_rate,json=falsePositiveRate,proto3" json:"false_positive_rate,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	// Output only. The Recall (True Positive Rate) when only considering the
	// label that has the highest prediction score and not below the confidence
	// threshold for each example.
	RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"`
	// Output only. The precision when only considering the label that has the
	// highest prediction score and not below the confidence threshold for each
	// example.
	PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"`
	// Output only. The False Positive Rate when only considering the label that
	// has the highest prediction score and not below the confidence threshold
	// for each example.
	FalsePositiveRateAt1 float32 `` /* 127-byte string literal not displayed */
	// Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
	F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"`
	// Output only. The number of model created labels that match a ground truth
	// label.
	TruePositiveCount int64 `protobuf:"varint,10,opt,name=true_positive_count,json=truePositiveCount,proto3" json:"true_positive_count,omitempty"`
	// Output only. The number of model created labels that do not match a
	// ground truth label.
	FalsePositiveCount int64 `protobuf:"varint,11,opt,name=false_positive_count,json=falsePositiveCount,proto3" json:"false_positive_count,omitempty"`
	// Output only. The number of ground truth labels that are not matched
	// by a model created label.
	FalseNegativeCount int64 `protobuf:"varint,12,opt,name=false_negative_count,json=falseNegativeCount,proto3" json:"false_negative_count,omitempty"`
	// Output only. The number of labels that were not created by the model,
	// but if they would, they would not match a ground truth label.
	TrueNegativeCount    int64    `protobuf:"varint,13,opt,name=true_negative_count,json=trueNegativeCount,proto3" json:"true_negative_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Metrics for a single confidence threshold.

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor 

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold() int32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage 

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() string

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_DiscardUnknown 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_DiscardUnknown()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Marshal 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Merge 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Merge(src proto.Message)

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Size 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Size() int

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Unmarshal 

func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Unmarshal(b []byte) error

type ClassificationEvaluationMetrics_ConfusionMatrix 

type ClassificationEvaluationMetrics_ConfusionMatrix struct {
	// Output only. IDs of the annotation specs used in the confusion matrix.
	// For Tables CLASSIFICATION
	//
	// [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
	// only list of [annotation_spec_display_name-s][] is populated.
	AnnotationSpecId []string `protobuf:"bytes,1,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// Output only. Display name of the annotation specs used in the confusion
	// matrix, as they were at the moment of the evaluation. For Tables
	// CLASSIFICATION
	//
	// [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
	// distinct values of the target column at the moment of the model
	// evaluation are populated here.
	DisplayName []string `protobuf:"bytes,3,rep,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only. Rows in the confusion matrix. The number of rows is equal to
	// the size of `annotation_spec_id`.
	// `row[i].example_count[j]` is the number of examples that have ground
	// truth of the `annotation_spec_id[i]` and are predicted as
	// `annotation_spec_id[j]` by the model being evaluated.
	Row                  []*ClassificationEvaluationMetrics_ConfusionMatrix_Row `protobuf:"bytes,2,rep,name=row,proto3" json:"row,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                                               `json:"-"`
	XXX_unrecognized     []byte                                                 `json:"-"`
	XXX_sizecache        int32                                                  `json:"-"`
}

Confusion matrix of the model running the classification.

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor 

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int)

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId() []string

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName() []string

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetRow 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) GetRow() []*ClassificationEvaluationMetrics_ConfusionMatrix_Row

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage 

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Reset 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) Reset()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) String 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) String() string

func (*ClassificationEvaluationMetrics_ConfusionMatrix) XXX_DiscardUnknown 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_DiscardUnknown()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Marshal 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Merge 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Merge(src proto.Message)

func (*ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Size 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Size() int

func (*ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Unmarshal 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Unmarshal(b []byte) error

type ClassificationEvaluationMetrics_ConfusionMatrix_Row 

type ClassificationEvaluationMetrics_ConfusionMatrix_Row struct {
	// Output only. Value of the specific cell in the confusion matrix.
	// The number of values each row has (i.e. the length of the row) is equal
	// to the length of the `annotation_spec_id` field or, if that one is not
	// populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
	ExampleCount         []int32  `protobuf:"varint,1,rep,packed,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Output only. A row in the confusion matrix.

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor 

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int)

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount() []int32

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage 

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) String 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() string

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_DiscardUnknown 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_DiscardUnknown()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Marshal 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Merge 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Merge(src proto.Message)

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Size 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Size() int

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Unmarshal 

func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Unmarshal(b []byte) error

type ClassificationType 

type ClassificationType int32

Type of the classification problem. 

const (
	// An un-set value of this enum.
	ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED ClassificationType = 0
	// At most one label is allowed per example.
	ClassificationType_MULTICLASS ClassificationType = 1
	// Multiple labels are allowed for one example.
	ClassificationType_MULTILABEL ClassificationType = 2
)

func (ClassificationType) EnumDescriptor 

func (ClassificationType) EnumDescriptor() ([]byte, []int)

func (ClassificationType) String 

func (x ClassificationType) String() string

type ColumnSpec 

type ColumnSpec struct {
	// Output only. The resource name of the column specs.
	// Form:
	//
	// `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}/columnSpecs/{column_spec_id}`
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// The data type of elements stored in the column.
	DataType *DataType `protobuf:"bytes,2,opt,name=data_type,json=dataType,proto3" json:"data_type,omitempty"`
	// Output only. The name of the column to show in the interface. The name can
	// be up to 100 characters long and can consist only of ASCII Latin letters
	// A-Z and a-z, ASCII digits 0-9, underscores(_), and forward slashes(/), and
	// must start with a letter or a digit.
	DisplayName string `protobuf:"bytes,3,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only. Stats of the series of values in the column.
	// This field may be stale, see the ancestor's
	// Dataset.tables_dataset_metadata.stats_update_time field
	// for the timestamp at which these stats were last updated.
	DataStats *DataStats `protobuf:"bytes,4,opt,name=data_stats,json=dataStats,proto3" json:"data_stats,omitempty"`
	// Deprecated.
	TopCorrelatedColumns []*ColumnSpec_CorrelatedColumn `protobuf:"bytes,5,rep,name=top_correlated_columns,json=topCorrelatedColumns,proto3" json:"top_correlated_columns,omitempty"`
	// Used to perform consistent read-modify-write updates. If not set, a blind
	// "overwrite" update happens.
	Etag                 string   `protobuf:"bytes,6,opt,name=etag,proto3" json:"etag,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by:

  • Tables

func (*ColumnSpec) Descriptor 

func (*ColumnSpec) Descriptor() ([]byte, []int)

func (*ColumnSpec) GetDataStats 

func (m *ColumnSpec) GetDataStats() *DataStats

func (*ColumnSpec) GetDataType 

func (m *ColumnSpec) GetDataType() *DataType

func (*ColumnSpec) GetDisplayName 

func (m *ColumnSpec) GetDisplayName() string

func (*ColumnSpec) GetEtag 

func (m *ColumnSpec) GetEtag() string

func (*ColumnSpec) GetName 

func (m *ColumnSpec) GetName() string

func (*ColumnSpec) GetTopCorrelatedColumns 

func (m *ColumnSpec) GetTopCorrelatedColumns() []*ColumnSpec_CorrelatedColumn

func (*ColumnSpec) ProtoMessage 

func (*ColumnSpec) ProtoMessage()

func (*ColumnSpec) Reset 

func (m *ColumnSpec) Reset()

func (*ColumnSpec) String 

func (m *ColumnSpec) String() string

func (*ColumnSpec) XXX_DiscardUnknown 

func (m *ColumnSpec) XXX_DiscardUnknown()

func (*ColumnSpec) XXX_Marshal 

func (m *ColumnSpec) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ColumnSpec) XXX_Merge 

func (m *ColumnSpec) XXX_Merge(src proto.Message)

func (*ColumnSpec) XXX_Size 

func (m *ColumnSpec) XXX_Size() int

func (*ColumnSpec) XXX_Unmarshal 

func (m *ColumnSpec) XXX_Unmarshal(b []byte) error

type ColumnSpec_CorrelatedColumn 

type ColumnSpec_CorrelatedColumn struct {
	// The column_spec_id of the correlated column, which belongs to the same
	// table as the in-context column.
	ColumnSpecId string `protobuf:"bytes,1,opt,name=column_spec_id,json=columnSpecId,proto3" json:"column_spec_id,omitempty"`
	// Correlation between this and the in-context column.
	CorrelationStats     *CorrelationStats `protobuf:"bytes,2,opt,name=correlation_stats,json=correlationStats,proto3" json:"correlation_stats,omitempty"`
	XXX_NoUnkeyedLiteral struct{}          `json:"-"`
	XXX_unrecognized     []byte            `json:"-"`
	XXX_sizecache        int32             `json:"-"`
}

Identifies the table's column, and its correlation with the column this ColumnSpec describes.

func (*ColumnSpec_CorrelatedColumn) Descriptor 

func (*ColumnSpec_CorrelatedColumn) Descriptor() ([]byte, []int)

func (*ColumnSpec_CorrelatedColumn) GetColumnSpecId 

func (m *ColumnSpec_CorrelatedColumn) GetColumnSpecId() string

func (*ColumnSpec_CorrelatedColumn) GetCorrelationStats 

func (m *ColumnSpec_CorrelatedColumn) GetCorrelationStats() *CorrelationStats

func (*ColumnSpec_CorrelatedColumn) ProtoMessage 

func (*ColumnSpec_CorrelatedColumn) ProtoMessage()

func (*ColumnSpec_CorrelatedColumn) Reset 

func (m *ColumnSpec_CorrelatedColumn) Reset()

func (*ColumnSpec_CorrelatedColumn) String 

func (m *ColumnSpec_CorrelatedColumn) String() string

func (*ColumnSpec_CorrelatedColumn) XXX_DiscardUnknown 

func (m *ColumnSpec_CorrelatedColumn) XXX_DiscardUnknown()

func (*ColumnSpec_CorrelatedColumn) XXX_Marshal 

func (m *ColumnSpec_CorrelatedColumn) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ColumnSpec_CorrelatedColumn) XXX_Merge 

func (m *ColumnSpec_CorrelatedColumn) XXX_Merge(src proto.Message)

func (*ColumnSpec_CorrelatedColumn) XXX_Size 

func (m *ColumnSpec_CorrelatedColumn) XXX_Size() int

func (*ColumnSpec_CorrelatedColumn) XXX_Unmarshal 

func (m *ColumnSpec_CorrelatedColumn) XXX_Unmarshal(b []byte) error

type CorrelationStats 

type CorrelationStats struct {
	// The correlation value using the Cramer's V measure.
	CramersV             float64  `protobuf:"fixed64,1,opt,name=cramers_v,json=cramersV,proto3" json:"cramers_v,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A correlation statistics between two series of DataType values. The series may have differing DataType-s, but within a single series the DataType must be the same.

func (*CorrelationStats) Descriptor 

func (*CorrelationStats) Descriptor() ([]byte, []int)

func (*CorrelationStats) GetCramersV 

func (m *CorrelationStats) GetCramersV() float64

func (*CorrelationStats) ProtoMessage 

func (*CorrelationStats) ProtoMessage()

func (*CorrelationStats) Reset 

func (m *CorrelationStats) Reset()

func (*CorrelationStats) String 

func (m *CorrelationStats) String() string

func (*CorrelationStats) XXX_DiscardUnknown 

func (m *CorrelationStats) XXX_DiscardUnknown()

func (*CorrelationStats) XXX_Marshal 

func (m *CorrelationStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*CorrelationStats) XXX_Merge 

func (m *CorrelationStats) XXX_Merge(src proto.Message)

func (*CorrelationStats) XXX_Size 

func (m *CorrelationStats) XXX_Size() int

func (*CorrelationStats) XXX_Unmarshal 

func (m *CorrelationStats) XXX_Unmarshal(b []byte) error

type CreateDatasetRequest 

type CreateDatasetRequest struct {
	// Required. The resource name of the project to create the dataset for.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Required. The dataset to create.
	Dataset              *Dataset `protobuf:"bytes,2,opt,name=dataset,proto3" json:"dataset,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.CreateDataset][google.cloud.automl.v1beta1.AutoMl.CreateDataset].

func (*CreateDatasetRequest) Descriptor 

func (*CreateDatasetRequest) Descriptor() ([]byte, []int)

func (*CreateDatasetRequest) GetDataset 

func (m *CreateDatasetRequest) GetDataset() *Dataset

func (*CreateDatasetRequest) GetParent 

func (m *CreateDatasetRequest) GetParent() string

func (*CreateDatasetRequest) ProtoMessage 

func (*CreateDatasetRequest) ProtoMessage()

func (*CreateDatasetRequest) Reset 

func (m *CreateDatasetRequest) Reset()

func (*CreateDatasetRequest) String 

func (m *CreateDatasetRequest) String() string

func (*CreateDatasetRequest) XXX_DiscardUnknown 

func (m *CreateDatasetRequest) XXX_DiscardUnknown()

func (*CreateDatasetRequest) XXX_Marshal 

func (m *CreateDatasetRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*CreateDatasetRequest) XXX_Merge 

func (m *CreateDatasetRequest) XXX_Merge(src proto.Message)

func (*CreateDatasetRequest) XXX_Size 

func (m *CreateDatasetRequest) XXX_Size() int

func (*CreateDatasetRequest) XXX_Unmarshal 

func (m *CreateDatasetRequest) XXX_Unmarshal(b []byte) error

type CreateModelOperationMetadata 

type CreateModelOperationMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Details of CreateModel operation.

func (*CreateModelOperationMetadata) Descriptor 

func (*CreateModelOperationMetadata) Descriptor() ([]byte, []int)

func (*CreateModelOperationMetadata) ProtoMessage 

func (*CreateModelOperationMetadata) ProtoMessage()

func (*CreateModelOperationMetadata) Reset 

func (m *CreateModelOperationMetadata) Reset()

func (*CreateModelOperationMetadata) String 

func (m *CreateModelOperationMetadata) String() string

func (*CreateModelOperationMetadata) XXX_DiscardUnknown 

func (m *CreateModelOperationMetadata) XXX_DiscardUnknown()

func (*CreateModelOperationMetadata) XXX_Marshal 

func (m *CreateModelOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*CreateModelOperationMetadata) XXX_Merge 

func (m *CreateModelOperationMetadata) XXX_Merge(src proto.Message)

func (*CreateModelOperationMetadata) XXX_Size 

func (m *CreateModelOperationMetadata) XXX_Size() int

func (*CreateModelOperationMetadata) XXX_Unmarshal 

func (m *CreateModelOperationMetadata) XXX_Unmarshal(b []byte) error

type CreateModelRequest 

type CreateModelRequest struct {
	// Required. Resource name of the parent project where the model is being created.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Required. The model to create.
	Model                *Model   `protobuf:"bytes,4,opt,name=model,proto3" json:"model,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.CreateModel][google.cloud.automl.v1beta1.AutoMl.CreateModel].

func (*CreateModelRequest) Descriptor 

func (*CreateModelRequest) Descriptor() ([]byte, []int)

func (*CreateModelRequest) GetModel 

func (m *CreateModelRequest) GetModel() *Model

func (*CreateModelRequest) GetParent 

func (m *CreateModelRequest) GetParent() string

func (*CreateModelRequest) ProtoMessage 

func (*CreateModelRequest) ProtoMessage()

func (*CreateModelRequest) Reset 

func (m *CreateModelRequest) Reset()

func (*CreateModelRequest) String 

func (m *CreateModelRequest) String() string

func (*CreateModelRequest) XXX_DiscardUnknown 

func (m *CreateModelRequest) XXX_DiscardUnknown()

func (*CreateModelRequest) XXX_Marshal 

func (m *CreateModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*CreateModelRequest) XXX_Merge 

func (m *CreateModelRequest) XXX_Merge(src proto.Message)

func (*CreateModelRequest) XXX_Size 

func (m *CreateModelRequest) XXX_Size() int

func (*CreateModelRequest) XXX_Unmarshal 

func (m *CreateModelRequest) XXX_Unmarshal(b []byte) error

type DataStats 

type DataStats struct {
	// The data statistics specific to a DataType.
	//
	// Types that are valid to be assigned to Stats:
	//	*DataStats_Float64Stats
	//	*DataStats_StringStats
	//	*DataStats_TimestampStats
	//	*DataStats_ArrayStats
	//	*DataStats_StructStats
	//	*DataStats_CategoryStats
	Stats isDataStats_Stats `protobuf_oneof:"stats"`
	// The number of distinct values.
	DistinctValueCount int64 `protobuf:"varint,1,opt,name=distinct_value_count,json=distinctValueCount,proto3" json:"distinct_value_count,omitempty"`
	// The number of values that are null.
	NullValueCount int64 `protobuf:"varint,2,opt,name=null_value_count,json=nullValueCount,proto3" json:"null_value_count,omitempty"`
	// The number of values that are valid.
	ValidValueCount      int64    `protobuf:"varint,9,opt,name=valid_value_count,json=validValueCount,proto3" json:"valid_value_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

The data statistics of a series of values that share the same DataType.

func (*DataStats) Descriptor 

func (*DataStats) Descriptor() ([]byte, []int)

func (*DataStats) GetArrayStats 

func (m *DataStats) GetArrayStats() *ArrayStats

func (*DataStats) GetCategoryStats 

func (m *DataStats) GetCategoryStats() *CategoryStats

func (*DataStats) GetDistinctValueCount 

func (m *DataStats) GetDistinctValueCount() int64

func (*DataStats) GetFloat64Stats 

func (m *DataStats) GetFloat64Stats() *Float64Stats

func (*DataStats) GetNullValueCount 

func (m *DataStats) GetNullValueCount() int64

func (*DataStats) GetStats 

func (m *DataStats) GetStats() isDataStats_Stats

func (*DataStats) GetStringStats 

func (m *DataStats) GetStringStats() *StringStats

func (*DataStats) GetStructStats 

func (m *DataStats) GetStructStats() *StructStats

func (*DataStats) GetTimestampStats 

func (m *DataStats) GetTimestampStats() *TimestampStats

func (*DataStats) GetValidValueCount 

func (m *DataStats) GetValidValueCount() int64

func (*DataStats) ProtoMessage 

func (*DataStats) ProtoMessage()

func (*DataStats) Reset 

func (m *DataStats) Reset()

func (*DataStats) String 

func (m *DataStats) String() string

func (*DataStats) XXX_DiscardUnknown 

func (m *DataStats) XXX_DiscardUnknown()

func (*DataStats) XXX_Marshal 

func (m *DataStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DataStats) XXX_Merge 

func (m *DataStats) XXX_Merge(src proto.Message)

func (*DataStats) XXX_OneofWrappers 

func (*DataStats) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*DataStats) XXX_Size 

func (m *DataStats) XXX_Size() int

func (*DataStats) XXX_Unmarshal 

func (m *DataStats) XXX_Unmarshal(b []byte) error

type DataStats_ArrayStats 

type DataStats_ArrayStats struct {
	ArrayStats *ArrayStats `protobuf:"bytes,6,opt,name=array_stats,json=arrayStats,proto3,oneof"`
}

type DataStats_CategoryStats 

type DataStats_CategoryStats struct {
	CategoryStats *CategoryStats `protobuf:"bytes,8,opt,name=category_stats,json=categoryStats,proto3,oneof"`
}

type DataStats_Float64Stats 

type DataStats_Float64Stats struct {
	Float64Stats *Float64Stats `protobuf:"bytes,3,opt,name=float64_stats,json=float64Stats,proto3,oneof"`
}

type DataStats_StringStats 

type DataStats_StringStats struct {
	StringStats *StringStats `protobuf:"bytes,4,opt,name=string_stats,json=stringStats,proto3,oneof"`
}

type DataStats_StructStats 

type DataStats_StructStats struct {
	StructStats *StructStats `protobuf:"bytes,7,opt,name=struct_stats,json=structStats,proto3,oneof"`
}

type DataStats_TimestampStats 

type DataStats_TimestampStats struct {
	TimestampStats *TimestampStats `protobuf:"bytes,5,opt,name=timestamp_stats,json=timestampStats,proto3,oneof"`
}

type DataType 

type DataType struct {
	// Details of DataType-s that need additional specification.
	//
	// Types that are valid to be assigned to Details:
	//	*DataType_ListElementType
	//	*DataType_StructType
	//	*DataType_TimeFormat
	Details isDataType_Details `protobuf_oneof:"details"`
	// Required. The [TypeCode][google.cloud.automl.v1beta1.TypeCode] for this type.
	TypeCode TypeCode `` /* 128-byte string literal not displayed */
	// If true, this DataType can also be `NULL`. In .CSV files `NULL` value is
	// expressed as an empty string.
	Nullable             bool     `protobuf:"varint,4,opt,name=nullable,proto3" json:"nullable,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Indicated the type of data that can be stored in a structured data entity (e.g. a table).

func (*DataType) Descriptor 

func (*DataType) Descriptor() ([]byte, []int)

func (*DataType) GetDetails 

func (m *DataType) GetDetails() isDataType_Details

func (*DataType) GetListElementType 

func (m *DataType) GetListElementType() *DataType

func (*DataType) GetNullable 

func (m *DataType) GetNullable() bool

func (*DataType) GetStructType 

func (m *DataType) GetStructType() *StructType

func (*DataType) GetTimeFormat 

func (m *DataType) GetTimeFormat() string

func (*DataType) GetTypeCode 

func (m *DataType) GetTypeCode() TypeCode

func (*DataType) ProtoMessage 

func (*DataType) ProtoMessage()

func (*DataType) Reset 

func (m *DataType) Reset()

func (*DataType) String 

func (m *DataType) String() string

func (*DataType) XXX_DiscardUnknown 

func (m *DataType) XXX_DiscardUnknown()

func (*DataType) XXX_Marshal 

func (m *DataType) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DataType) XXX_Merge 

func (m *DataType) XXX_Merge(src proto.Message)

func (*DataType) XXX_OneofWrappers 

func (*DataType) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*DataType) XXX_Size 

func (m *DataType) XXX_Size() int

func (*DataType) XXX_Unmarshal 

func (m *DataType) XXX_Unmarshal(b []byte) error

type DataType_ListElementType 

type DataType_ListElementType struct {
	ListElementType *DataType `protobuf:"bytes,2,opt,name=list_element_type,json=listElementType,proto3,oneof"`
}

type DataType_StructType 

type DataType_StructType struct {
	StructType *StructType `protobuf:"bytes,3,opt,name=struct_type,json=structType,proto3,oneof"`
}

type DataType_TimeFormat 

type DataType_TimeFormat struct {
	TimeFormat string `protobuf:"bytes,5,opt,name=time_format,json=timeFormat,proto3,oneof"`
}

type Dataset 

type Dataset struct {
	// Required.
	// The dataset metadata that is specific to the problem type.
	//
	// Types that are valid to be assigned to DatasetMetadata:
	//	*Dataset_TranslationDatasetMetadata
	//	*Dataset_ImageClassificationDatasetMetadata
	//	*Dataset_TextClassificationDatasetMetadata
	//	*Dataset_ImageObjectDetectionDatasetMetadata
	//	*Dataset_VideoClassificationDatasetMetadata
	//	*Dataset_VideoObjectTrackingDatasetMetadata
	//	*Dataset_TextExtractionDatasetMetadata
	//	*Dataset_TextSentimentDatasetMetadata
	//	*Dataset_TablesDatasetMetadata
	DatasetMetadata isDataset_DatasetMetadata `protobuf_oneof:"dataset_metadata"`
	// Output only. The resource name of the dataset.
	// Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The name of the dataset to show in the interface. The name can be
	// up to 32 characters long and can consist only of ASCII Latin letters A-Z
	// and a-z, underscores
	// (_), and ASCII digits 0-9.
	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// User-provided description of the dataset. The description can be up to
	// 25000 characters long.
	Description string `protobuf:"bytes,3,opt,name=description,proto3" json:"description,omitempty"`
	// Output only. The number of examples in the dataset.
	ExampleCount int32 `protobuf:"varint,21,opt,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
	// Output only. Timestamp when this dataset was created.
	CreateTime *timestamp.Timestamp `protobuf:"bytes,14,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
	// Used to perform consistent read-modify-write updates. If not set, a blind
	// "overwrite" update happens.
	Etag                 string   `protobuf:"bytes,17,opt,name=etag,proto3" json:"etag,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

func (*Dataset) Descriptor 

func (*Dataset) Descriptor() ([]byte, []int)

func (*Dataset) GetCreateTime 

func (m *Dataset) GetCreateTime() *timestamp.Timestamp

func (*Dataset) GetDatasetMetadata 

func (m *Dataset) GetDatasetMetadata() isDataset_DatasetMetadata

func (*Dataset) GetDescription 

func (m *Dataset) GetDescription() string

func (*Dataset) GetDisplayName 

func (m *Dataset) GetDisplayName() string

func (*Dataset) GetEtag 

func (m *Dataset) GetEtag() string

func (*Dataset) GetExampleCount 

func (m *Dataset) GetExampleCount() int32

func (*Dataset) GetImageClassificationDatasetMetadata 

func (m *Dataset) GetImageClassificationDatasetMetadata() *ImageClassificationDatasetMetadata

func (*Dataset) GetImageObjectDetectionDatasetMetadata 

func (m *Dataset) GetImageObjectDetectionDatasetMetadata() *ImageObjectDetectionDatasetMetadata

func (*Dataset) GetName 

func (m *Dataset) GetName() string

func (*Dataset) GetTablesDatasetMetadata 

func (m *Dataset) GetTablesDatasetMetadata() *TablesDatasetMetadata

func (*Dataset) GetTextClassificationDatasetMetadata 

func (m *Dataset) GetTextClassificationDatasetMetadata() *TextClassificationDatasetMetadata

func (*Dataset) GetTextExtractionDatasetMetadata 

func (m *Dataset) GetTextExtractionDatasetMetadata() *TextExtractionDatasetMetadata

func (*Dataset) GetTextSentimentDatasetMetadata 

func (m *Dataset) GetTextSentimentDatasetMetadata() *TextSentimentDatasetMetadata

func (*Dataset) GetTranslationDatasetMetadata 

func (m *Dataset) GetTranslationDatasetMetadata() *TranslationDatasetMetadata

func (*Dataset) GetVideoClassificationDatasetMetadata 

func (m *Dataset) GetVideoClassificationDatasetMetadata() *VideoClassificationDatasetMetadata

func (*Dataset) GetVideoObjectTrackingDatasetMetadata 

func (m *Dataset) GetVideoObjectTrackingDatasetMetadata() *VideoObjectTrackingDatasetMetadata

func (*Dataset) ProtoMessage 

func (*Dataset) ProtoMessage()

func (*Dataset) Reset 

func (m *Dataset) Reset()

func (*Dataset) String 

func (m *Dataset) String() string

func (*Dataset) XXX_DiscardUnknown 

func (m *Dataset) XXX_DiscardUnknown()

func (*Dataset) XXX_Marshal 

func (m *Dataset) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*Dataset) XXX_Merge 

func (m *Dataset) XXX_Merge(src proto.Message)

func (*Dataset) XXX_OneofWrappers 

func (*Dataset) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*Dataset) XXX_Size 

func (m *Dataset) XXX_Size() int

func (*Dataset) XXX_Unmarshal 

func (m *Dataset) XXX_Unmarshal(b []byte) error

type Dataset_ImageClassificationDatasetMetadata 

type Dataset_ImageClassificationDatasetMetadata struct {
	ImageClassificationDatasetMetadata *ImageClassificationDatasetMetadata `protobuf:"bytes,24,opt,name=image_classification_dataset_metadata,json=imageClassificationDatasetMetadata,proto3,oneof"`
}

type Dataset_ImageObjectDetectionDatasetMetadata 

type Dataset_ImageObjectDetectionDatasetMetadata struct {
	ImageObjectDetectionDatasetMetadata *ImageObjectDetectionDatasetMetadata `protobuf:"bytes,26,opt,name=image_object_detection_dataset_metadata,json=imageObjectDetectionDatasetMetadata,proto3,oneof"`
}

type Dataset_TablesDatasetMetadata 

type Dataset_TablesDatasetMetadata struct {
	TablesDatasetMetadata *TablesDatasetMetadata `protobuf:"bytes,33,opt,name=tables_dataset_metadata,json=tablesDatasetMetadata,proto3,oneof"`
}

type Dataset_TextClassificationDatasetMetadata 

type Dataset_TextClassificationDatasetMetadata struct {
	TextClassificationDatasetMetadata *TextClassificationDatasetMetadata `protobuf:"bytes,25,opt,name=text_classification_dataset_metadata,json=textClassificationDatasetMetadata,proto3,oneof"`
}

type Dataset_TextExtractionDatasetMetadata 

type Dataset_TextExtractionDatasetMetadata struct {
	TextExtractionDatasetMetadata *TextExtractionDatasetMetadata `protobuf:"bytes,28,opt,name=text_extraction_dataset_metadata,json=textExtractionDatasetMetadata,proto3,oneof"`
}

type Dataset_TextSentimentDatasetMetadata 

type Dataset_TextSentimentDatasetMetadata struct {
	TextSentimentDatasetMetadata *TextSentimentDatasetMetadata `protobuf:"bytes,30,opt,name=text_sentiment_dataset_metadata,json=textSentimentDatasetMetadata,proto3,oneof"`
}

type Dataset_TranslationDatasetMetadata 

type Dataset_TranslationDatasetMetadata struct {
	TranslationDatasetMetadata *TranslationDatasetMetadata `protobuf:"bytes,23,opt,name=translation_dataset_metadata,json=translationDatasetMetadata,proto3,oneof"`
}

type Dataset_VideoClassificationDatasetMetadata 

type Dataset_VideoClassificationDatasetMetadata struct {
	VideoClassificationDatasetMetadata *VideoClassificationDatasetMetadata `protobuf:"bytes,31,opt,name=video_classification_dataset_metadata,json=videoClassificationDatasetMetadata,proto3,oneof"`
}

type Dataset_VideoObjectTrackingDatasetMetadata 

type Dataset_VideoObjectTrackingDatasetMetadata struct {
	VideoObjectTrackingDatasetMetadata *VideoObjectTrackingDatasetMetadata `protobuf:"bytes,29,opt,name=video_object_tracking_dataset_metadata,json=videoObjectTrackingDatasetMetadata,proto3,oneof"`
}

type DeleteDatasetRequest 

type DeleteDatasetRequest struct {
	// Required. The resource name of the dataset to delete.
	Name                 string   `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1beta1.AutoMl.DeleteDataset].

func (*DeleteDatasetRequest) Descriptor 

func (*DeleteDatasetRequest) Descriptor() ([]byte, []int)

func (*DeleteDatasetRequest) GetName 

func (m *DeleteDatasetRequest) GetName() string

func (*DeleteDatasetRequest) ProtoMessage 

func (*DeleteDatasetRequest) ProtoMessage()

func (*DeleteDatasetRequest) Reset 

func (m *DeleteDatasetRequest) Reset()

func (*DeleteDatasetRequest) String 

func (m *DeleteDatasetRequest) String() string

func (*DeleteDatasetRequest) XXX_DiscardUnknown 

func (m *DeleteDatasetRequest) XXX_DiscardUnknown()

func (*DeleteDatasetRequest) XXX_Marshal 

func (m *DeleteDatasetRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DeleteDatasetRequest) XXX_Merge 

func (m *DeleteDatasetRequest) XXX_Merge(src proto.Message)

func (*DeleteDatasetRequest) XXX_Size 

func (m *DeleteDatasetRequest) XXX_Size() int

func (*DeleteDatasetRequest) XXX_Unmarshal 

func (m *DeleteDatasetRequest) XXX_Unmarshal(b []byte) error

type DeleteModelRequest 

type DeleteModelRequest struct {
	// Required. Resource name of the model being deleted.
	Name                 string   `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.DeleteModel][google.cloud.automl.v1beta1.AutoMl.DeleteModel].

func (*DeleteModelRequest) Descriptor 

func (*DeleteModelRequest) Descriptor() ([]byte, []int)

func (*DeleteModelRequest) GetName 

func (m *DeleteModelRequest) GetName() string

func (*DeleteModelRequest) ProtoMessage 

func (*DeleteModelRequest) ProtoMessage()

func (*DeleteModelRequest) Reset 

func (m *DeleteModelRequest) Reset()

func (*DeleteModelRequest) String 

func (m *DeleteModelRequest) String() string

func (*DeleteModelRequest) XXX_DiscardUnknown 

func (m *DeleteModelRequest) XXX_DiscardUnknown()

func (*DeleteModelRequest) XXX_Marshal 

func (m *DeleteModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DeleteModelRequest) XXX_Merge 

func (m *DeleteModelRequest) XXX_Merge(src proto.Message)

func (*DeleteModelRequest) XXX_Size 

func (m *DeleteModelRequest) XXX_Size() int

func (*DeleteModelRequest) XXX_Unmarshal 

func (m *DeleteModelRequest) XXX_Unmarshal(b []byte) error

type DeleteOperationMetadata 

type DeleteOperationMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Details of operations that perform deletes of any entities.

func (*DeleteOperationMetadata) Descriptor 

func (*DeleteOperationMetadata) Descriptor() ([]byte, []int)

func (*DeleteOperationMetadata) ProtoMessage 

func (*DeleteOperationMetadata) ProtoMessage()

func (*DeleteOperationMetadata) Reset 

func (m *DeleteOperationMetadata) Reset()

func (*DeleteOperationMetadata) String 

func (m *DeleteOperationMetadata) String() string

func (*DeleteOperationMetadata) XXX_DiscardUnknown 

func (m *DeleteOperationMetadata) XXX_DiscardUnknown()

func (*DeleteOperationMetadata) XXX_Marshal 

func (m *DeleteOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DeleteOperationMetadata) XXX_Merge 

func (m *DeleteOperationMetadata) XXX_Merge(src proto.Message)

func (*DeleteOperationMetadata) XXX_Size 

func (m *DeleteOperationMetadata) XXX_Size() int

func (*DeleteOperationMetadata) XXX_Unmarshal 

func (m *DeleteOperationMetadata) XXX_Unmarshal(b []byte) error

type DeployModelOperationMetadata 

type DeployModelOperationMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Details of DeployModel operation.

func (*DeployModelOperationMetadata) Descriptor 

func (*DeployModelOperationMetadata) Descriptor() ([]byte, []int)

func (*DeployModelOperationMetadata) ProtoMessage 

func (*DeployModelOperationMetadata) ProtoMessage()

func (*DeployModelOperationMetadata) Reset 

func (m *DeployModelOperationMetadata) Reset()

func (*DeployModelOperationMetadata) String 

func (m *DeployModelOperationMetadata) String() string

func (*DeployModelOperationMetadata) XXX_DiscardUnknown 

func (m *DeployModelOperationMetadata) XXX_DiscardUnknown()

func (*DeployModelOperationMetadata) XXX_Marshal 

func (m *DeployModelOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DeployModelOperationMetadata) XXX_Merge 

func (m *DeployModelOperationMetadata) XXX_Merge(src proto.Message)

func (*DeployModelOperationMetadata) XXX_Size 

func (m *DeployModelOperationMetadata) XXX_Size() int

func (*DeployModelOperationMetadata) XXX_Unmarshal 

func (m *DeployModelOperationMetadata) XXX_Unmarshal(b []byte) error

type DeployModelRequest 

type DeployModelRequest struct {
	// The per-domain specific deployment parameters.
	//
	// Types that are valid to be assigned to ModelDeploymentMetadata:
	//	*DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata
	//	*DeployModelRequest_ImageClassificationModelDeploymentMetadata
	ModelDeploymentMetadata isDeployModelRequest_ModelDeploymentMetadata `protobuf_oneof:"model_deployment_metadata"`
	// Required. Resource name of the model to deploy.
	Name                 string   `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.DeployModel][google.cloud.automl.v1beta1.AutoMl.DeployModel].

func (*DeployModelRequest) Descriptor 

func (*DeployModelRequest) Descriptor() ([]byte, []int)

func (*DeployModelRequest) GetImageClassificationModelDeploymentMetadata 

func (m *DeployModelRequest) GetImageClassificationModelDeploymentMetadata() *ImageClassificationModelDeploymentMetadata

func (*DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata 

func (m *DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata() *ImageObjectDetectionModelDeploymentMetadata

func (*DeployModelRequest) GetModelDeploymentMetadata 

func (m *DeployModelRequest) GetModelDeploymentMetadata() isDeployModelRequest_ModelDeploymentMetadata

func (*DeployModelRequest) GetName 

func (m *DeployModelRequest) GetName() string

func (*DeployModelRequest) ProtoMessage 

func (*DeployModelRequest) ProtoMessage()

func (*DeployModelRequest) Reset 

func (m *DeployModelRequest) Reset()

func (*DeployModelRequest) String 

func (m *DeployModelRequest) String() string

func (*DeployModelRequest) XXX_DiscardUnknown 

func (m *DeployModelRequest) XXX_DiscardUnknown()

func (*DeployModelRequest) XXX_Marshal 

func (m *DeployModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DeployModelRequest) XXX_Merge 

func (m *DeployModelRequest) XXX_Merge(src proto.Message)

func (*DeployModelRequest) XXX_OneofWrappers 

func (*DeployModelRequest) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*DeployModelRequest) XXX_Size 

func (m *DeployModelRequest) XXX_Size() int

func (*DeployModelRequest) XXX_Unmarshal 

func (m *DeployModelRequest) XXX_Unmarshal(b []byte) error

type DeployModelRequest_ImageClassificationModelDeploymentMetadata 

type DeployModelRequest_ImageClassificationModelDeploymentMetadata struct {
	ImageClassificationModelDeploymentMetadata *ImageClassificationModelDeploymentMetadata `` /* 135-byte string literal not displayed */
}

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata 

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata struct {
	ImageObjectDetectionModelDeploymentMetadata *ImageObjectDetectionModelDeploymentMetadata `` /* 138-byte string literal not displayed */
}

type Document 

type Document struct {
	// An input config specifying the content of the document.
	InputConfig *DocumentInputConfig `protobuf:"bytes,1,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// The plain text version of this document.
	DocumentText *TextSnippet `protobuf:"bytes,2,opt,name=document_text,json=documentText,proto3" json:"document_text,omitempty"`
	// Describes the layout of the document.
	// Sorted by [page_number][].
	Layout []*Document_Layout `protobuf:"bytes,3,rep,name=layout,proto3" json:"layout,omitempty"`
	// The dimensions of the page in the document.
	DocumentDimensions *DocumentDimensions `protobuf:"bytes,4,opt,name=document_dimensions,json=documentDimensions,proto3" json:"document_dimensions,omitempty"`
	// Number of pages in the document.
	PageCount            int32    `protobuf:"varint,5,opt,name=page_count,json=pageCount,proto3" json:"page_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A structured text document e.g. a PDF.

func (*Document) Descriptor 

func (*Document) Descriptor() ([]byte, []int)

func (*Document) GetDocumentDimensions 

func (m *Document) GetDocumentDimensions() *DocumentDimensions

func (*Document) GetDocumentText 

func (m *Document) GetDocumentText() *TextSnippet

func (*Document) GetInputConfig 

func (m *Document) GetInputConfig() *DocumentInputConfig

func (*Document) GetLayout 

func (m *Document) GetLayout() []*Document_Layout

func (*Document) GetPageCount 

func (m *Document) GetPageCount() int32

func (*Document) ProtoMessage 

func (*Document) ProtoMessage()

func (*Document) Reset 

func (m *Document) Reset()

func (*Document) String 

func (m *Document) String() string

func (*Document) XXX_DiscardUnknown 

func (m *Document) XXX_DiscardUnknown()

func (*Document) XXX_Marshal 

func (m *Document) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*Document) XXX_Merge 

func (m *Document) XXX_Merge(src proto.Message)

func (*Document) XXX_Size 

func (m *Document) XXX_Size() int

func (*Document) XXX_Unmarshal 

func (m *Document) XXX_Unmarshal(b []byte) error

type DocumentDimensions 

type DocumentDimensions struct {
	// Unit of the dimension.
	Unit DocumentDimensions_DocumentDimensionUnit `` /* 136-byte string literal not displayed */
	// Width value of the document, works together with the unit.
	Width float32 `protobuf:"fixed32,2,opt,name=width,proto3" json:"width,omitempty"`
	// Height value of the document, works together with the unit.
	Height               float32  `protobuf:"fixed32,3,opt,name=height,proto3" json:"height,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Message that describes dimension of a document.

func (*DocumentDimensions) Descriptor 

func (*DocumentDimensions) Descriptor() ([]byte, []int)

func (*DocumentDimensions) GetHeight 

func (m *DocumentDimensions) GetHeight() float32

func (*DocumentDimensions) GetUnit 

func (m *DocumentDimensions) GetUnit() DocumentDimensions_DocumentDimensionUnit

func (*DocumentDimensions) GetWidth 

func (m *DocumentDimensions) GetWidth() float32

func (*DocumentDimensions) ProtoMessage 

func (*DocumentDimensions) ProtoMessage()

func (*DocumentDimensions) Reset 

func (m *DocumentDimensions) Reset()

func (*DocumentDimensions) String 

func (m *DocumentDimensions) String() string

func (*DocumentDimensions) XXX_DiscardUnknown 

func (m *DocumentDimensions) XXX_DiscardUnknown()

func (*DocumentDimensions) XXX_Marshal 

func (m *DocumentDimensions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DocumentDimensions) XXX_Merge 

func (m *DocumentDimensions) XXX_Merge(src proto.Message)

func (*DocumentDimensions) XXX_Size 

func (m *DocumentDimensions) XXX_Size() int

func (*DocumentDimensions) XXX_Unmarshal 

func (m *DocumentDimensions) XXX_Unmarshal(b []byte) error

type DocumentDimensions_DocumentDimensionUnit 

type DocumentDimensions_DocumentDimensionUnit int32

Unit of the document dimension. 

const (
	// Should not be used.
	DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED DocumentDimensions_DocumentDimensionUnit = 0
	// Document dimension is measured in inches.
	DocumentDimensions_INCH DocumentDimensions_DocumentDimensionUnit = 1
	// Document dimension is measured in centimeters.
	DocumentDimensions_CENTIMETER DocumentDimensions_DocumentDimensionUnit = 2
	// Document dimension is measured in points. 72 points = 1 inch.
	DocumentDimensions_POINT DocumentDimensions_DocumentDimensionUnit = 3
)

func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor 

func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor() ([]byte, []int)

func (DocumentDimensions_DocumentDimensionUnit) String 

func (x DocumentDimensions_DocumentDimensionUnit) String() string

type DocumentInputConfig 

type DocumentInputConfig struct {
	// The Google Cloud Storage location of the document file. Only a single path
	// should be given.
	// Max supported size: 512MB.
	// Supported extensions: .PDF.
	GcsSource            *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3" json:"gcs_source,omitempty"`
	XXX_NoUnkeyedLiteral struct{}   `json:"-"`
	XXX_unrecognized     []byte     `json:"-"`
	XXX_sizecache        int32      `json:"-"`
}

Input configuration of a Document[google.cloud.automl.v1beta1.Document].

func (*DocumentInputConfig) Descriptor 

func (*DocumentInputConfig) Descriptor() ([]byte, []int)

func (*DocumentInputConfig) GetGcsSource 

func (m *DocumentInputConfig) GetGcsSource() *GcsSource

func (*DocumentInputConfig) ProtoMessage 

func (*DocumentInputConfig) ProtoMessage()

func (*DocumentInputConfig) Reset 

func (m *DocumentInputConfig) Reset()

func (*DocumentInputConfig) String 

func (m *DocumentInputConfig) String() string

func (*DocumentInputConfig) XXX_DiscardUnknown 

func (m *DocumentInputConfig) XXX_DiscardUnknown()

func (*DocumentInputConfig) XXX_Marshal 

func (m *DocumentInputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DocumentInputConfig) XXX_Merge 

func (m *DocumentInputConfig) XXX_Merge(src proto.Message)

func (*DocumentInputConfig) XXX_Size 

func (m *DocumentInputConfig) XXX_Size() int

func (*DocumentInputConfig) XXX_Unmarshal 

func (m *DocumentInputConfig) XXX_Unmarshal(b []byte) error

type Document_Layout 

type Document_Layout struct {
	// Text Segment that represents a segment in
	// [document_text][google.cloud.automl.v1beta1.Document.document_text].
	TextSegment *TextSegment `protobuf:"bytes,1,opt,name=text_segment,json=textSegment,proto3" json:"text_segment,omitempty"`
	// Page number of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the original document, starts
	// from 1.
	PageNumber int32 `protobuf:"varint,2,opt,name=page_number,json=pageNumber,proto3" json:"page_number,omitempty"`
	// The position of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the page.
	// Contains exactly 4
	//
	// [normalized_vertices][google.cloud.automl.v1beta1.BoundingPoly.normalized_vertices]
	// and they are connected by edges in the order provided, which will
	// represent a rectangle parallel to the frame. The
	// [NormalizedVertex-s][google.cloud.automl.v1beta1.NormalizedVertex] are
	// relative to the page.
	// Coordinates are based on top-left as point (0,0).
	BoundingPoly *BoundingPoly `protobuf:"bytes,3,opt,name=bounding_poly,json=boundingPoly,proto3" json:"bounding_poly,omitempty"`
	// The type of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in document.
	TextSegmentType      Document_Layout_TextSegmentType `` /* 174-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}                        `json:"-"`
	XXX_unrecognized     []byte                          `json:"-"`
	XXX_sizecache        int32                           `json:"-"`
}

Describes the layout information of a [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the document.

func (*Document_Layout) Descriptor 

func (*Document_Layout) Descriptor() ([]byte, []int)

func (*Document_Layout) GetBoundingPoly 

func (m *Document_Layout) GetBoundingPoly() *BoundingPoly

func (*Document_Layout) GetPageNumber 

func (m *Document_Layout) GetPageNumber() int32

func (*Document_Layout) GetTextSegment 

func (m *Document_Layout) GetTextSegment() *TextSegment

func (*Document_Layout) GetTextSegmentType 

func (m *Document_Layout) GetTextSegmentType() Document_Layout_TextSegmentType

func (*Document_Layout) ProtoMessage 

func (*Document_Layout) ProtoMessage()

func (*Document_Layout) Reset 

func (m *Document_Layout) Reset()

func (*Document_Layout) String 

func (m *Document_Layout) String() string

func (*Document_Layout) XXX_DiscardUnknown 

func (m *Document_Layout) XXX_DiscardUnknown()

func (*Document_Layout) XXX_Marshal 

func (m *Document_Layout) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*Document_Layout) XXX_Merge 

func (m *Document_Layout) XXX_Merge(src proto.Message)

func (*Document_Layout) XXX_Size 

func (m *Document_Layout) XXX_Size() int

func (*Document_Layout) XXX_Unmarshal 

func (m *Document_Layout) XXX_Unmarshal(b []byte) error

type Document_Layout_TextSegmentType 

type Document_Layout_TextSegmentType int32

The type of TextSegment in the context of the original document. 

const (
	// Should not be used.
	Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED Document_Layout_TextSegmentType = 0
	// The text segment is a token. e.g. word.
	Document_Layout_TOKEN Document_Layout_TextSegmentType = 1
	// The text segment is a paragraph.
	Document_Layout_PARAGRAPH Document_Layout_TextSegmentType = 2
	// The text segment is a form field.
	Document_Layout_FORM_FIELD Document_Layout_TextSegmentType = 3
	// The text segment is the name part of a form field. It will be treated
	// as child of another FORM_FIELD TextSegment if its span is subspan of
	// another TextSegment with type FORM_FIELD.
	Document_Layout_FORM_FIELD_NAME Document_Layout_TextSegmentType = 4
	// The text segment is the text content part of a form field. It will be
	// treated as child of another FORM_FIELD TextSegment if its span is
	// subspan of another TextSegment with type FORM_FIELD.
	Document_Layout_FORM_FIELD_CONTENTS Document_Layout_TextSegmentType = 5
	// The text segment is a whole table, including headers, and all rows.
	Document_Layout_TABLE Document_Layout_TextSegmentType = 6
	// The text segment is a table's headers. It will be treated as child of
	// another TABLE TextSegment if its span is subspan of another TextSegment
	// with type TABLE.
	Document_Layout_TABLE_HEADER Document_Layout_TextSegmentType = 7
	// The text segment is a row in table. It will be treated as child of
	// another TABLE TextSegment if its span is subspan of another TextSegment
	// with type TABLE.
	Document_Layout_TABLE_ROW Document_Layout_TextSegmentType = 8
	// The text segment is a cell in table. It will be treated as child of
	// another TABLE_ROW TextSegment if its span is subspan of another
	// TextSegment with type TABLE_ROW.
	Document_Layout_TABLE_CELL Document_Layout_TextSegmentType = 9
)

func (Document_Layout_TextSegmentType) EnumDescriptor 

func (Document_Layout_TextSegmentType) EnumDescriptor() ([]byte, []int)

func (Document_Layout_TextSegmentType) String 

func (x Document_Layout_TextSegmentType) String() string

type DoubleRange 

type DoubleRange struct {
	// Start of the range, inclusive.
	Start float64 `protobuf:"fixed64,1,opt,name=start,proto3" json:"start,omitempty"`
	// End of the range, exclusive.
	End                  float64  `protobuf:"fixed64,2,opt,name=end,proto3" json:"end,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A range between two double numbers.

func (*DoubleRange) Descriptor 

func (*DoubleRange) Descriptor() ([]byte, []int)

func (*DoubleRange) GetEnd 

func (m *DoubleRange) GetEnd() float64

func (*DoubleRange) GetStart 

func (m *DoubleRange) GetStart() float64

func (*DoubleRange) ProtoMessage 

func (*DoubleRange) ProtoMessage()

func (*DoubleRange) Reset 

func (m *DoubleRange) Reset()

func (*DoubleRange) String 

func (m *DoubleRange) String() string

func (*DoubleRange) XXX_DiscardUnknown 

func (m *DoubleRange) XXX_DiscardUnknown()

func (*DoubleRange) XXX_Marshal 

func (m *DoubleRange) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*DoubleRange) XXX_Merge 

func (m *DoubleRange) XXX_Merge(src proto.Message)

func (*DoubleRange) XXX_Size 

func (m *DoubleRange) XXX_Size() int

func (*DoubleRange) XXX_Unmarshal 

func (m *DoubleRange) XXX_Unmarshal(b []byte) error

type ExamplePayload 

type ExamplePayload struct {
	// Required. Input only. The example data.
	//
	// Types that are valid to be assigned to Payload:
	//	*ExamplePayload_Image
	//	*ExamplePayload_TextSnippet
	//	*ExamplePayload_Document
	//	*ExamplePayload_Row
	Payload              isExamplePayload_Payload `protobuf_oneof:"payload"`
	XXX_NoUnkeyedLiteral struct{}                 `json:"-"`
	XXX_unrecognized     []byte                   `json:"-"`
	XXX_sizecache        int32                    `json:"-"`
}

Example data used for training or prediction.

func (*ExamplePayload) Descriptor 

func (*ExamplePayload) Descriptor() ([]byte, []int)

func (*ExamplePayload) GetDocument 

func (m *ExamplePayload) GetDocument() *Document

func (*ExamplePayload) GetImage 

func (m *ExamplePayload) GetImage() *Image

func (*ExamplePayload) GetPayload 

func (m *ExamplePayload) GetPayload() isExamplePayload_Payload

func (*ExamplePayload) GetRow 

func (m *ExamplePayload) GetRow() *Row

func (*ExamplePayload) GetTextSnippet 

func (m *ExamplePayload) GetTextSnippet() *TextSnippet

func (*ExamplePayload) ProtoMessage 

func (*ExamplePayload) ProtoMessage()

func (*ExamplePayload) Reset 

func (m *ExamplePayload) Reset()

func (*ExamplePayload) String 

func (m *ExamplePayload) String() string

func (*ExamplePayload) XXX_DiscardUnknown 

func (m *ExamplePayload) XXX_DiscardUnknown()

func (*ExamplePayload) XXX_Marshal 

func (m *ExamplePayload) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExamplePayload) XXX_Merge 

func (m *ExamplePayload) XXX_Merge(src proto.Message)

func (*ExamplePayload) XXX_OneofWrappers 

func (*ExamplePayload) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*ExamplePayload) XXX_Size 

func (m *ExamplePayload) XXX_Size() int

func (*ExamplePayload) XXX_Unmarshal 

func (m *ExamplePayload) XXX_Unmarshal(b []byte) error

type ExamplePayload_Document 

type ExamplePayload_Document struct {
	Document *Document `protobuf:"bytes,4,opt,name=document,proto3,oneof"`
}

type ExamplePayload_Image 

type ExamplePayload_Image struct {
	Image *Image `protobuf:"bytes,1,opt,name=image,proto3,oneof"`
}

type ExamplePayload_Row 

type ExamplePayload_Row struct {
	Row *Row `protobuf:"bytes,3,opt,name=row,proto3,oneof"`
}

type ExamplePayload_TextSnippet 

type ExamplePayload_TextSnippet struct {
	TextSnippet *TextSnippet `protobuf:"bytes,2,opt,name=text_snippet,json=textSnippet,proto3,oneof"`
}

type ExportDataOperationMetadata 

type ExportDataOperationMetadata struct {
	// Output only. Information further describing this export data's output.
	OutputInfo           *ExportDataOperationMetadata_ExportDataOutputInfo `protobuf:"bytes,1,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                                          `json:"-"`
	XXX_unrecognized     []byte                                            `json:"-"`
	XXX_sizecache        int32                                             `json:"-"`
}

Details of ExportData operation.

func (*ExportDataOperationMetadata) Descriptor 

func (*ExportDataOperationMetadata) Descriptor() ([]byte, []int)

func (*ExportDataOperationMetadata) GetOutputInfo 

func (m *ExportDataOperationMetadata) GetOutputInfo() *ExportDataOperationMetadata_ExportDataOutputInfo

func (*ExportDataOperationMetadata) ProtoMessage 

func (*ExportDataOperationMetadata) ProtoMessage()

func (*ExportDataOperationMetadata) Reset 

func (m *ExportDataOperationMetadata) Reset()

func (*ExportDataOperationMetadata) String 

func (m *ExportDataOperationMetadata) String() string

func (*ExportDataOperationMetadata) XXX_DiscardUnknown 

func (m *ExportDataOperationMetadata) XXX_DiscardUnknown()

func (*ExportDataOperationMetadata) XXX_Marshal 

func (m *ExportDataOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportDataOperationMetadata) XXX_Merge 

func (m *ExportDataOperationMetadata) XXX_Merge(src proto.Message)

func (*ExportDataOperationMetadata) XXX_Size 

func (m *ExportDataOperationMetadata) XXX_Size() int

func (*ExportDataOperationMetadata) XXX_Unmarshal 

func (m *ExportDataOperationMetadata) XXX_Unmarshal(b []byte) error

type ExportDataOperationMetadata_ExportDataOutputInfo 

type ExportDataOperationMetadata_ExportDataOutputInfo struct {
	// The output location to which the exported data is written.
	//
	// Types that are valid to be assigned to OutputLocation:
	//	*ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory
	//	*ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset
	OutputLocation       isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation `protobuf_oneof:"output_location"`
	XXX_NoUnkeyedLiteral struct{}                                                          `json:"-"`
	XXX_unrecognized     []byte                                                            `json:"-"`
	XXX_sizecache        int32                                                             `json:"-"`
}

Further describes this export data's output. Supplements OutputConfig[google.cloud.automl.v1beta1.OutputConfig].

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Descriptor 

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Descriptor() ([]byte, []int)

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetBigqueryOutputDataset 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) GetBigqueryOutputDataset() string

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory() string

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation() isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage 

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Reset 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) Reset()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) String 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) String() string

func (*ExportDataOperationMetadata_ExportDataOutputInfo) XXX_DiscardUnknown 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) XXX_DiscardUnknown()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) XXX_Marshal 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportDataOperationMetadata_ExportDataOutputInfo) XXX_Merge 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) XXX_Merge(src proto.Message)

func (*ExportDataOperationMetadata_ExportDataOutputInfo) XXX_OneofWrappers 

func (*ExportDataOperationMetadata_ExportDataOutputInfo) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*ExportDataOperationMetadata_ExportDataOutputInfo) XXX_Size 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) XXX_Size() int

func (*ExportDataOperationMetadata_ExportDataOutputInfo) XXX_Unmarshal 

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) XXX_Unmarshal(b []byte) error

type ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset 

type ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset struct {
	BigqueryOutputDataset string `protobuf:"bytes,2,opt,name=bigquery_output_dataset,json=bigqueryOutputDataset,proto3,oneof"`
}

type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory 

type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory struct {
	GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3,oneof"`
}

type ExportDataRequest 

type ExportDataRequest struct {
	// Required. The resource name of the dataset.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired output location.
	OutputConfig         *OutputConfig `protobuf:"bytes,3,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	XXX_NoUnkeyedLiteral struct{}      `json:"-"`
	XXX_unrecognized     []byte        `json:"-"`
	XXX_sizecache        int32         `json:"-"`
}

Request message for [AutoMl.ExportData][google.cloud.automl.v1beta1.AutoMl.ExportData].

func (*ExportDataRequest) Descriptor 

func (*ExportDataRequest) Descriptor() ([]byte, []int)

func (*ExportDataRequest) GetName 

func (m *ExportDataRequest) GetName() string

func (*ExportDataRequest) GetOutputConfig 

func (m *ExportDataRequest) GetOutputConfig() *OutputConfig

func (*ExportDataRequest) ProtoMessage 

func (*ExportDataRequest) ProtoMessage()

func (*ExportDataRequest) Reset 

func (m *ExportDataRequest) Reset()

func (*ExportDataRequest) String 

func (m *ExportDataRequest) String() string

func (*ExportDataRequest) XXX_DiscardUnknown 

func (m *ExportDataRequest) XXX_DiscardUnknown()

func (*ExportDataRequest) XXX_Marshal 

func (m *ExportDataRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportDataRequest) XXX_Merge 

func (m *ExportDataRequest) XXX_Merge(src proto.Message)

func (*ExportDataRequest) XXX_Size 

func (m *ExportDataRequest) XXX_Size() int

func (*ExportDataRequest) XXX_Unmarshal 

func (m *ExportDataRequest) XXX_Unmarshal(b []byte) error

type ExportEvaluatedExamplesOperationMetadata 

type ExportEvaluatedExamplesOperationMetadata struct {
	// Output only. Information further describing the output of this evaluated
	// examples export.
	OutputInfo           *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo `protobuf:"bytes,2,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                                                                    `json:"-"`
	XXX_unrecognized     []byte                                                                      `json:"-"`
	XXX_sizecache        int32                                                                       `json:"-"`
}

Details of EvaluatedExamples operation.

func (*ExportEvaluatedExamplesOperationMetadata) Descriptor 

func (*ExportEvaluatedExamplesOperationMetadata) Descriptor() ([]byte, []int)

func (*ExportEvaluatedExamplesOperationMetadata) GetOutputInfo 

func (m *ExportEvaluatedExamplesOperationMetadata) GetOutputInfo() *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo

func (*ExportEvaluatedExamplesOperationMetadata) ProtoMessage 

func (*ExportEvaluatedExamplesOperationMetadata) ProtoMessage()

func (*ExportEvaluatedExamplesOperationMetadata) Reset 

func (m *ExportEvaluatedExamplesOperationMetadata) Reset()

func (*ExportEvaluatedExamplesOperationMetadata) String 

func (m *ExportEvaluatedExamplesOperationMetadata) String() string

func (*ExportEvaluatedExamplesOperationMetadata) XXX_DiscardUnknown 

func (m *ExportEvaluatedExamplesOperationMetadata) XXX_DiscardUnknown()

func (*ExportEvaluatedExamplesOperationMetadata) XXX_Marshal 

func (m *ExportEvaluatedExamplesOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportEvaluatedExamplesOperationMetadata) XXX_Merge 

func (m *ExportEvaluatedExamplesOperationMetadata) XXX_Merge(src proto.Message)

func (*ExportEvaluatedExamplesOperationMetadata) XXX_Size 

func (m *ExportEvaluatedExamplesOperationMetadata) XXX_Size() int

func (*ExportEvaluatedExamplesOperationMetadata) XXX_Unmarshal 

func (m *ExportEvaluatedExamplesOperationMetadata) XXX_Unmarshal(b []byte) error

type ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo 

type ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo struct {
	// The path of the BigQuery dataset created, in bq://projectId.bqDatasetId
	// format, into which the output of export evaluated examples is written.
	BigqueryOutputDataset string   `` /* 126-byte string literal not displayed */
	XXX_NoUnkeyedLiteral  struct{} `json:"-"`
	XXX_unrecognized      []byte   `json:"-"`
	XXX_sizecache         int32    `json:"-"`
}

Further describes the output of the evaluated examples export. Supplements

ExportEvaluatedExamplesOutputConfig[google.cloud.automl.v1beta1.ExportEvaluatedExamplesOutputConfig].

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) Descriptor 

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) Descriptor() ([]byte, []int)

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) GetBigqueryOutputDataset 

func (m *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) GetBigqueryOutputDataset() string

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoMessage 

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoMessage()

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) Reset 

func (m *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) Reset()

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) String 

func (m *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) String() string

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_DiscardUnknown 

func (m *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_DiscardUnknown()

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_Marshal 

func (m *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_Merge 

func (m *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_Merge(src proto.Message)

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_Size 

func (m *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_Size() int

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_Unmarshal 

func (m *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) XXX_Unmarshal(b []byte) error

type ExportEvaluatedExamplesOutputConfig 

type ExportEvaluatedExamplesOutputConfig struct {
	// Required. The destination of the output.
	//
	// Types that are valid to be assigned to Destination:
	//	*ExportEvaluatedExamplesOutputConfig_BigqueryDestination
	Destination          isExportEvaluatedExamplesOutputConfig_Destination `protobuf_oneof:"destination"`
	XXX_NoUnkeyedLiteral struct{}                                          `json:"-"`
	XXX_unrecognized     []byte                                            `json:"-"`
	XXX_sizecache        int32                                             `json:"-"`
}

Output configuration for ExportEvaluatedExamples Action. Note that this call is available only for 30 days since the moment the model was evaluated. The output depends on the domain, as follows (note that only examples from the TEST set are exported):

  • For Tables:

[bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination] 

pointing to a BigQuery project must be set. In the given project a
new dataset will be created with name

`export_evaluated_examples_<model-display-name>_<timestamp-of-export-call>` 

where <model-display-name> will be made BigQuery-dataset-name
compatible (e.g. most special characters will become underscores),
and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601"
format. In the dataset an `evaluated_examples` table will be
created. It will have all the same columns as the

[primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id] 

of the
[dataset][google.cloud.automl.v1beta1.Model.dataset_id] from which
the model was created, as they were at the moment of model's
evaluation (this includes the target column with its ground
truth), followed by a column called "predicted_<target_column>". That
last column will contain the model's prediction result for each
respective row, given as ARRAY of
[AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload],
represented as STRUCT-s, containing
[TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].

func (*ExportEvaluatedExamplesOutputConfig) Descriptor 

func (*ExportEvaluatedExamplesOutputConfig) Descriptor() ([]byte, []int)

func (*ExportEvaluatedExamplesOutputConfig) GetBigqueryDestination 

func (m *ExportEvaluatedExamplesOutputConfig) GetBigqueryDestination() *BigQueryDestination

func (*ExportEvaluatedExamplesOutputConfig) GetDestination 

func (m *ExportEvaluatedExamplesOutputConfig) GetDestination() isExportEvaluatedExamplesOutputConfig_Destination

func (*ExportEvaluatedExamplesOutputConfig) ProtoMessage 

func (*ExportEvaluatedExamplesOutputConfig) ProtoMessage()

func (*ExportEvaluatedExamplesOutputConfig) Reset 

func (m *ExportEvaluatedExamplesOutputConfig) Reset()

func (*ExportEvaluatedExamplesOutputConfig) String 

func (m *ExportEvaluatedExamplesOutputConfig) String() string

func (*ExportEvaluatedExamplesOutputConfig) XXX_DiscardUnknown 

func (m *ExportEvaluatedExamplesOutputConfig) XXX_DiscardUnknown()

func (*ExportEvaluatedExamplesOutputConfig) XXX_Marshal 

func (m *ExportEvaluatedExamplesOutputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportEvaluatedExamplesOutputConfig) XXX_Merge 

func (m *ExportEvaluatedExamplesOutputConfig) XXX_Merge(src proto.Message)

func (*ExportEvaluatedExamplesOutputConfig) XXX_OneofWrappers 

func (*ExportEvaluatedExamplesOutputConfig) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*ExportEvaluatedExamplesOutputConfig) XXX_Size 

func (m *ExportEvaluatedExamplesOutputConfig) XXX_Size() int

func (*ExportEvaluatedExamplesOutputConfig) XXX_Unmarshal 

func (m *ExportEvaluatedExamplesOutputConfig) XXX_Unmarshal(b []byte) error

type ExportEvaluatedExamplesOutputConfig_BigqueryDestination 

type ExportEvaluatedExamplesOutputConfig_BigqueryDestination struct {
	BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}

type ExportEvaluatedExamplesRequest 

type ExportEvaluatedExamplesRequest struct {
	// Required. The resource name of the model whose evaluated examples are to
	// be exported.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired output location and configuration.
	OutputConfig         *ExportEvaluatedExamplesOutputConfig `protobuf:"bytes,3,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                             `json:"-"`
	XXX_unrecognized     []byte                               `json:"-"`
	XXX_sizecache        int32                                `json:"-"`
}

Request message for [AutoMl.ExportEvaluatedExamples][google.cloud.automl.v1beta1.AutoMl.ExportEvaluatedExamples].

func (*ExportEvaluatedExamplesRequest) Descriptor 

func (*ExportEvaluatedExamplesRequest) Descriptor() ([]byte, []int)

func (*ExportEvaluatedExamplesRequest) GetName 

func (m *ExportEvaluatedExamplesRequest) GetName() string

func (*ExportEvaluatedExamplesRequest) GetOutputConfig 

func (m *ExportEvaluatedExamplesRequest) GetOutputConfig() *ExportEvaluatedExamplesOutputConfig

func (*ExportEvaluatedExamplesRequest) ProtoMessage 

func (*ExportEvaluatedExamplesRequest) ProtoMessage()

func (*ExportEvaluatedExamplesRequest) Reset 

func (m *ExportEvaluatedExamplesRequest) Reset()

func (*ExportEvaluatedExamplesRequest) String 

func (m *ExportEvaluatedExamplesRequest) String() string

func (*ExportEvaluatedExamplesRequest) XXX_DiscardUnknown 

func (m *ExportEvaluatedExamplesRequest) XXX_DiscardUnknown()

func (*ExportEvaluatedExamplesRequest) XXX_Marshal 

func (m *ExportEvaluatedExamplesRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportEvaluatedExamplesRequest) XXX_Merge 

func (m *ExportEvaluatedExamplesRequest) XXX_Merge(src proto.Message)

func (*ExportEvaluatedExamplesRequest) XXX_Size 

func (m *ExportEvaluatedExamplesRequest) XXX_Size() int

func (*ExportEvaluatedExamplesRequest) XXX_Unmarshal 

func (m *ExportEvaluatedExamplesRequest) XXX_Unmarshal(b []byte) error

type ExportModelOperationMetadata 

type ExportModelOperationMetadata struct {
	// Output only. Information further describing the output of this model
	// export.
	OutputInfo           *ExportModelOperationMetadata_ExportModelOutputInfo `protobuf:"bytes,2,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                                            `json:"-"`
	XXX_unrecognized     []byte                                              `json:"-"`
	XXX_sizecache        int32                                               `json:"-"`
}

Details of ExportModel operation.

func (*ExportModelOperationMetadata) Descriptor 

func (*ExportModelOperationMetadata) Descriptor() ([]byte, []int)

func (*ExportModelOperationMetadata) GetOutputInfo 

func (m *ExportModelOperationMetadata) GetOutputInfo() *ExportModelOperationMetadata_ExportModelOutputInfo

func (*ExportModelOperationMetadata) ProtoMessage 

func (*ExportModelOperationMetadata) ProtoMessage()

func (*ExportModelOperationMetadata) Reset 

func (m *ExportModelOperationMetadata) Reset()

func (*ExportModelOperationMetadata) String 

func (m *ExportModelOperationMetadata) String() string

func (*ExportModelOperationMetadata) XXX_DiscardUnknown 

func (m *ExportModelOperationMetadata) XXX_DiscardUnknown()

func (*ExportModelOperationMetadata) XXX_Marshal 

func (m *ExportModelOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportModelOperationMetadata) XXX_Merge 

func (m *ExportModelOperationMetadata) XXX_Merge(src proto.Message)

func (*ExportModelOperationMetadata) XXX_Size 

func (m *ExportModelOperationMetadata) XXX_Size() int

func (*ExportModelOperationMetadata) XXX_Unmarshal 

func (m *ExportModelOperationMetadata) XXX_Unmarshal(b []byte) error

type ExportModelOperationMetadata_ExportModelOutputInfo 

type ExportModelOperationMetadata_ExportModelOutputInfo struct {
	// The full path of the Google Cloud Storage directory created, into which
	// the model will be exported.
	GcsOutputDirectory   string   `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3" json:"gcs_output_directory,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Further describes the output of model export. Supplements

ModelExportOutputConfig[google.cloud.automl.v1beta1.ModelExportOutputConfig].

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Descriptor 

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Descriptor() ([]byte, []int)

func (*ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory 

func (m *ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory() string

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage 

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Reset 

func (m *ExportModelOperationMetadata_ExportModelOutputInfo) Reset()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) String 

func (m *ExportModelOperationMetadata_ExportModelOutputInfo) String() string

func (*ExportModelOperationMetadata_ExportModelOutputInfo) XXX_DiscardUnknown 

func (m *ExportModelOperationMetadata_ExportModelOutputInfo) XXX_DiscardUnknown()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) XXX_Marshal 

func (m *ExportModelOperationMetadata_ExportModelOutputInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportModelOperationMetadata_ExportModelOutputInfo) XXX_Merge 

func (m *ExportModelOperationMetadata_ExportModelOutputInfo) XXX_Merge(src proto.Message)

func (*ExportModelOperationMetadata_ExportModelOutputInfo) XXX_Size 

func (m *ExportModelOperationMetadata_ExportModelOutputInfo) XXX_Size() int

func (*ExportModelOperationMetadata_ExportModelOutputInfo) XXX_Unmarshal 

func (m *ExportModelOperationMetadata_ExportModelOutputInfo) XXX_Unmarshal(b []byte) error

type ExportModelRequest 

type ExportModelRequest struct {
	// Required. The resource name of the model to export.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired output location and configuration.
	OutputConfig         *ModelExportOutputConfig `protobuf:"bytes,3,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                 `json:"-"`
	XXX_unrecognized     []byte                   `json:"-"`
	XXX_sizecache        int32                    `json:"-"`
}

Request message for [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned.

func (*ExportModelRequest) Descriptor 

func (*ExportModelRequest) Descriptor() ([]byte, []int)

func (*ExportModelRequest) GetName 

func (m *ExportModelRequest) GetName() string

func (*ExportModelRequest) GetOutputConfig 

func (m *ExportModelRequest) GetOutputConfig() *ModelExportOutputConfig

func (*ExportModelRequest) ProtoMessage 

func (*ExportModelRequest) ProtoMessage()

func (*ExportModelRequest) Reset 

func (m *ExportModelRequest) Reset()

func (*ExportModelRequest) String 

func (m *ExportModelRequest) String() string

func (*ExportModelRequest) XXX_DiscardUnknown 

func (m *ExportModelRequest) XXX_DiscardUnknown()

func (*ExportModelRequest) XXX_Marshal 

func (m *ExportModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ExportModelRequest) XXX_Merge 

func (m *ExportModelRequest) XXX_Merge(src proto.Message)

func (*ExportModelRequest) XXX_Size 

func (m *ExportModelRequest) XXX_Size() int

func (*ExportModelRequest) XXX_Unmarshal 

func (m *ExportModelRequest) XXX_Unmarshal(b []byte) error

type Float64Stats 

type Float64Stats struct {
	// The mean of the series.
	Mean float64 `protobuf:"fixed64,1,opt,name=mean,proto3" json:"mean,omitempty"`
	// The standard deviation of the series.
	StandardDeviation float64 `protobuf:"fixed64,2,opt,name=standard_deviation,json=standardDeviation,proto3" json:"standard_deviation,omitempty"`
	// Ordered from 0 to k k-quantile values of the data series of n values.
	// The value at index i is, approximately, the i*n/k-th smallest value in the
	// series; for i = 0 and i = k these are, respectively, the min and max
	// values.
	Quantiles []float64 `protobuf:"fixed64,3,rep,packed,name=quantiles,proto3" json:"quantiles,omitempty"`
	// Histogram buckets of the data series. Sorted by the min value of the
	// bucket, ascendingly, and the number of the buckets is dynamically
	// generated. The buckets are non-overlapping and completely cover whole
	// FLOAT64 range with min of first bucket being `"-Infinity"`, and max of
	// the last one being `"Infinity"`.
	HistogramBuckets     []*Float64Stats_HistogramBucket `protobuf:"bytes,4,rep,name=histogram_buckets,json=histogramBuckets,proto3" json:"histogram_buckets,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                        `json:"-"`
	XXX_unrecognized     []byte                          `json:"-"`
	XXX_sizecache        int32                           `json:"-"`
}

The data statistics of a series of FLOAT64 values.

func (*Float64Stats) Descriptor 

func (*Float64Stats) Descriptor() ([]byte, []int)

func (*Float64Stats) GetHistogramBuckets 

func (m *Float64Stats) GetHistogramBuckets() []*Float64Stats_HistogramBucket

func (*Float64Stats) GetMean 

func (m *Float64Stats) GetMean() float64

func (*Float64Stats) GetQuantiles 

func (m *Float64Stats) GetQuantiles() []float64

func (*Float64Stats) GetStandardDeviation 

func (m *Float64Stats) GetStandardDeviation() float64

func (*Float64Stats) ProtoMessage 

func (*Float64Stats) ProtoMessage()

func (*Float64Stats) Reset 

func (m *Float64Stats) Reset()

func (*Float64Stats) String 

func (m *Float64Stats) String() string

func (*Float64Stats) XXX_DiscardUnknown 

func (m *Float64Stats) XXX_DiscardUnknown()

func (*Float64Stats) XXX_Marshal 

func (m *Float64Stats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*Float64Stats) XXX_Merge 

func (m *Float64Stats) XXX_Merge(src proto.Message)

func (*Float64Stats) XXX_Size 

func (m *Float64Stats) XXX_Size() int

func (*Float64Stats) XXX_Unmarshal 

func (m *Float64Stats) XXX_Unmarshal(b []byte) error

type Float64Stats_HistogramBucket 

type Float64Stats_HistogramBucket struct {
	// The minimum value of the bucket, inclusive.
	Min float64 `protobuf:"fixed64,1,opt,name=min,proto3" json:"min,omitempty"`
	// The maximum value of the bucket, exclusive unless max = `"Infinity"`, in
	// which case it's inclusive.
	Max float64 `protobuf:"fixed64,2,opt,name=max,proto3" json:"max,omitempty"`
	// The number of data values that are in the bucket, i.e. are between
	// min and max values.
	Count                int64    `protobuf:"varint,3,opt,name=count,proto3" json:"count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A bucket of a histogram.

func (*Float64Stats_HistogramBucket) Descriptor 

func (*Float64Stats_HistogramBucket) Descriptor() ([]byte, []int)

func (*Float64Stats_HistogramBucket) GetCount 

func (m *Float64Stats_HistogramBucket) GetCount() int64

func (*Float64Stats_HistogramBucket) GetMax 

func (m *Float64Stats_HistogramBucket) GetMax() float64

func (*Float64Stats_HistogramBucket) GetMin 

func (m *Float64Stats_HistogramBucket) GetMin() float64

func (*Float64Stats_HistogramBucket) ProtoMessage 

func (*Float64Stats_HistogramBucket) ProtoMessage()

func (*Float64Stats_HistogramBucket) Reset 

func (m *Float64Stats_HistogramBucket) Reset()

func (*Float64Stats_HistogramBucket) String 

func (m *Float64Stats_HistogramBucket) String() string

func (*Float64Stats_HistogramBucket) XXX_DiscardUnknown 

func (m *Float64Stats_HistogramBucket) XXX_DiscardUnknown()

func (*Float64Stats_HistogramBucket) XXX_Marshal 

func (m *Float64Stats_HistogramBucket) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*Float64Stats_HistogramBucket) XXX_Merge 

func (m *Float64Stats_HistogramBucket) XXX_Merge(src proto.Message)

func (*Float64Stats_HistogramBucket) XXX_Size 

func (m *Float64Stats_HistogramBucket) XXX_Size() int

func (*Float64Stats_HistogramBucket) XXX_Unmarshal 

func (m *Float64Stats_HistogramBucket) XXX_Unmarshal(b []byte) error

type GcrDestination 

type GcrDestination struct {
	// Required. Google Contained Registry URI of the new image, up to 2000
	// characters long. See
	//
	// https:
	// //cloud.google.com/container-registry/do
	// // cs/pushing-and-pulling#pushing_an_image_to_a_registry
	// Accepted forms:
	// * [HOSTNAME]/[PROJECT-ID]/[IMAGE]
	// * [HOSTNAME]/[PROJECT-ID]/[IMAGE]:[TAG]
	//
	// The requesting user must have permission to push images the project.
	OutputUri            string   `protobuf:"bytes,1,opt,name=output_uri,json=outputUri,proto3" json:"output_uri,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

The GCR location where the image must be pushed to.

func (*GcrDestination) Descriptor 

func (*GcrDestination) Descriptor() ([]byte, []int)

func (*GcrDestination) GetOutputUri 

func (m *GcrDestination) GetOutputUri() string

func (*GcrDestination) ProtoMessage 

func (*GcrDestination) ProtoMessage()

func (*GcrDestination) Reset 

func (m *GcrDestination) Reset()

func (*GcrDestination) String 

func (m *GcrDestination) String() string

func (*GcrDestination) XXX_DiscardUnknown 

func (m *GcrDestination) XXX_DiscardUnknown()

func (*GcrDestination) XXX_Marshal 

func (m *GcrDestination) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GcrDestination) XXX_Merge 

func (m *GcrDestination) XXX_Merge(src proto.Message)

func (*GcrDestination) XXX_Size 

func (m *GcrDestination) XXX_Size() int

func (*GcrDestination) XXX_Unmarshal 

func (m *GcrDestination) XXX_Unmarshal(b []byte) error

type GcsDestination 

type GcsDestination struct {
	// Required. Google Cloud Storage URI to output directory, up to 2000
	// characters long.
	// Accepted forms:
	// * Prefix path: gs://bucket/directory
	// The requesting user must have write permission to the bucket.
	// The directory is created if it doesn't exist.
	OutputUriPrefix      string   `protobuf:"bytes,1,opt,name=output_uri_prefix,json=outputUriPrefix,proto3" json:"output_uri_prefix,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

The Google Cloud Storage location where the output is to be written to.

func (*GcsDestination) Descriptor 

func (*GcsDestination) Descriptor() ([]byte, []int)

func (*GcsDestination) GetOutputUriPrefix 

func (m *GcsDestination) GetOutputUriPrefix() string

func (*GcsDestination) ProtoMessage 

func (*GcsDestination) ProtoMessage()

func (*GcsDestination) Reset 

func (m *GcsDestination) Reset()

func (*GcsDestination) String 

func (m *GcsDestination) String() string

func (*GcsDestination) XXX_DiscardUnknown 

func (m *GcsDestination) XXX_DiscardUnknown()

func (*GcsDestination) XXX_Marshal 

func (m *GcsDestination) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GcsDestination) XXX_Merge 

func (m *GcsDestination) XXX_Merge(src proto.Message)

func (*GcsDestination) XXX_Size 

func (m *GcsDestination) XXX_Size() int

func (*GcsDestination) XXX_Unmarshal 

func (m *GcsDestination) XXX_Unmarshal(b []byte) error

type GcsSource 

type GcsSource struct {
	// Required. Google Cloud Storage URIs to input files, up to 2000 characters
	// long. Accepted forms:
	// * Full object path, e.g. gs://bucket/directory/object.csv
	InputUris            []string `protobuf:"bytes,1,rep,name=input_uris,json=inputUris,proto3" json:"input_uris,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

The Google Cloud Storage location for the input content.

func (*GcsSource) Descriptor 

func (*GcsSource) Descriptor() ([]byte, []int)

func (*GcsSource) GetInputUris 

func (m *GcsSource) GetInputUris() []string

func (*GcsSource) ProtoMessage 

func (*GcsSource) ProtoMessage()

func (*GcsSource) Reset 

func (m *GcsSource) Reset()

func (*GcsSource) String 

func (m *GcsSource) String() string

func (*GcsSource) XXX_DiscardUnknown 

func (m *GcsSource) XXX_DiscardUnknown()

func (*GcsSource) XXX_Marshal 

func (m *GcsSource) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GcsSource) XXX_Merge 

func (m *GcsSource) XXX_Merge(src proto.Message)

func (*GcsSource) XXX_Size 

func (m *GcsSource) XXX_Size() int

func (*GcsSource) XXX_Unmarshal 

func (m *GcsSource) XXX_Unmarshal(b []byte) error

type GetAnnotationSpecRequest 

type GetAnnotationSpecRequest struct {
	// Required. The resource name of the annotation spec to retrieve.
	Name                 string   `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1beta1.AutoMl.GetAnnotationSpec].

func (*GetAnnotationSpecRequest) Descriptor 

func (*GetAnnotationSpecRequest) Descriptor() ([]byte, []int)

func (*GetAnnotationSpecRequest) GetName 

func (m *GetAnnotationSpecRequest) GetName() string

func (*GetAnnotationSpecRequest) ProtoMessage 

func (*GetAnnotationSpecRequest) ProtoMessage()

func (*GetAnnotationSpecRequest) Reset 

func (m *GetAnnotationSpecRequest) Reset()

func (*GetAnnotationSpecRequest) String 

func (m *GetAnnotationSpecRequest) String() string

func (*GetAnnotationSpecRequest) XXX_DiscardUnknown 

func (m *GetAnnotationSpecRequest) XXX_DiscardUnknown()

func (*GetAnnotationSpecRequest) XXX_Marshal 

func (m *GetAnnotationSpecRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GetAnnotationSpecRequest) XXX_Merge 

func (m *GetAnnotationSpecRequest) XXX_Merge(src proto.Message)

func (*GetAnnotationSpecRequest) XXX_Size 

func (m *GetAnnotationSpecRequest) XXX_Size() int

func (*GetAnnotationSpecRequest) XXX_Unmarshal 

func (m *GetAnnotationSpecRequest) XXX_Unmarshal(b []byte) error

type GetColumnSpecRequest 

type GetColumnSpecRequest struct {
	// Required. The resource name of the column spec to retrieve.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Mask specifying which fields to read.
	FieldMask            *field_mask.FieldMask `protobuf:"bytes,2,opt,name=field_mask,json=fieldMask,proto3" json:"field_mask,omitempty"`
	XXX_NoUnkeyedLiteral struct{}              `json:"-"`
	XXX_unrecognized     []byte                `json:"-"`
	XXX_sizecache        int32                 `json:"-"`
}

Request message for [AutoMl.GetColumnSpec][google.cloud.automl.v1beta1.AutoMl.GetColumnSpec].

func (*GetColumnSpecRequest) Descriptor 

func (*GetColumnSpecRequest) Descriptor() ([]byte, []int)

func (*GetColumnSpecRequest) GetFieldMask 

func (m *GetColumnSpecRequest) GetFieldMask() *field_mask.FieldMask

func (*GetColumnSpecRequest) GetName 

func (m *GetColumnSpecRequest) GetName() string

func (*GetColumnSpecRequest) ProtoMessage 

func (*GetColumnSpecRequest) ProtoMessage()

func (*GetColumnSpecRequest) Reset 

func (m *GetColumnSpecRequest) Reset()

func (*GetColumnSpecRequest) String 

func (m *GetColumnSpecRequest) String() string

func (*GetColumnSpecRequest) XXX_DiscardUnknown 

func (m *GetColumnSpecRequest) XXX_DiscardUnknown()

func (*GetColumnSpecRequest) XXX_Marshal 

func (m *GetColumnSpecRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GetColumnSpecRequest) XXX_Merge 

func (m *GetColumnSpecRequest) XXX_Merge(src proto.Message)

func (*GetColumnSpecRequest) XXX_Size 

func (m *GetColumnSpecRequest) XXX_Size() int

func (*GetColumnSpecRequest) XXX_Unmarshal 

func (m *GetColumnSpecRequest) XXX_Unmarshal(b []byte) error

type GetDatasetRequest 

type GetDatasetRequest struct {
	// Required. The resource name of the dataset to retrieve.
	Name                 string   `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.GetDataset][google.cloud.automl.v1beta1.AutoMl.GetDataset].

func (*GetDatasetRequest) Descriptor 

func (*GetDatasetRequest) Descriptor() ([]byte, []int)

func (*GetDatasetRequest) GetName 

func (m *GetDatasetRequest) GetName() string

func (*GetDatasetRequest) ProtoMessage 

func (*GetDatasetRequest) ProtoMessage()

func (*GetDatasetRequest) Reset 

func (m *GetDatasetRequest) Reset()

func (*GetDatasetRequest) String 

func (m *GetDatasetRequest) String() string

func (*GetDatasetRequest) XXX_DiscardUnknown 

func (m *GetDatasetRequest) XXX_DiscardUnknown()

func (*GetDatasetRequest) XXX_Marshal 

func (m *GetDatasetRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GetDatasetRequest) XXX_Merge 

func (m *GetDatasetRequest) XXX_Merge(src proto.Message)

func (*GetDatasetRequest) XXX_Size 

func (m *GetDatasetRequest) XXX_Size() int

func (*GetDatasetRequest) XXX_Unmarshal 

func (m *GetDatasetRequest) XXX_Unmarshal(b []byte) error

type GetModelEvaluationRequest 

type GetModelEvaluationRequest struct {
	// Required. Resource name for the model evaluation.
	Name                 string   `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1beta1.AutoMl.GetModelEvaluation].

func (*GetModelEvaluationRequest) Descriptor 

func (*GetModelEvaluationRequest) Descriptor() ([]byte, []int)

func (*GetModelEvaluationRequest) GetName 

func (m *GetModelEvaluationRequest) GetName() string

func (*GetModelEvaluationRequest) ProtoMessage 

func (*GetModelEvaluationRequest) ProtoMessage()

func (*GetModelEvaluationRequest) Reset 

func (m *GetModelEvaluationRequest) Reset()

func (*GetModelEvaluationRequest) String 

func (m *GetModelEvaluationRequest) String() string

func (*GetModelEvaluationRequest) XXX_DiscardUnknown 

func (m *GetModelEvaluationRequest) XXX_DiscardUnknown()

func (*GetModelEvaluationRequest) XXX_Marshal 

func (m *GetModelEvaluationRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GetModelEvaluationRequest) XXX_Merge 

func (m *GetModelEvaluationRequest) XXX_Merge(src proto.Message)

func (*GetModelEvaluationRequest) XXX_Size 

func (m *GetModelEvaluationRequest) XXX_Size() int

func (*GetModelEvaluationRequest) XXX_Unmarshal 

func (m *GetModelEvaluationRequest) XXX_Unmarshal(b []byte) error

type GetModelRequest 

type GetModelRequest struct {
	// Required. Resource name of the model.
	Name                 string   `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.GetModel][google.cloud.automl.v1beta1.AutoMl.GetModel].

func (*GetModelRequest) Descriptor 

func (*GetModelRequest) Descriptor() ([]byte, []int)

func (*GetModelRequest) GetName 

func (m *GetModelRequest) GetName() string

func (*GetModelRequest) ProtoMessage 

func (*GetModelRequest) ProtoMessage()

func (*GetModelRequest) Reset 

func (m *GetModelRequest) Reset()

func (*GetModelRequest) String 

func (m *GetModelRequest) String() string

func (*GetModelRequest) XXX_DiscardUnknown 

func (m *GetModelRequest) XXX_DiscardUnknown()

func (*GetModelRequest) XXX_Marshal 

func (m *GetModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GetModelRequest) XXX_Merge 

func (m *GetModelRequest) XXX_Merge(src proto.Message)

func (*GetModelRequest) XXX_Size 

func (m *GetModelRequest) XXX_Size() int

func (*GetModelRequest) XXX_Unmarshal 

func (m *GetModelRequest) XXX_Unmarshal(b []byte) error

type GetTableSpecRequest 

type GetTableSpecRequest struct {
	// Required. The resource name of the table spec to retrieve.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Mask specifying which fields to read.
	FieldMask            *field_mask.FieldMask `protobuf:"bytes,2,opt,name=field_mask,json=fieldMask,proto3" json:"field_mask,omitempty"`
	XXX_NoUnkeyedLiteral struct{}              `json:"-"`
	XXX_unrecognized     []byte                `json:"-"`
	XXX_sizecache        int32                 `json:"-"`
}

Request message for [AutoMl.GetTableSpec][google.cloud.automl.v1beta1.AutoMl.GetTableSpec].

func (*GetTableSpecRequest) Descriptor 

func (*GetTableSpecRequest) Descriptor() ([]byte, []int)

func (*GetTableSpecRequest) GetFieldMask 

func (m *GetTableSpecRequest) GetFieldMask() *field_mask.FieldMask

func (*GetTableSpecRequest) GetName 

func (m *GetTableSpecRequest) GetName() string

func (*GetTableSpecRequest) ProtoMessage 

func (*GetTableSpecRequest) ProtoMessage()

func (*GetTableSpecRequest) Reset 

func (m *GetTableSpecRequest) Reset()

func (*GetTableSpecRequest) String 

func (m *GetTableSpecRequest) String() string

func (*GetTableSpecRequest) XXX_DiscardUnknown 

func (m *GetTableSpecRequest) XXX_DiscardUnknown()

func (*GetTableSpecRequest) XXX_Marshal 

func (m *GetTableSpecRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*GetTableSpecRequest) XXX_Merge 

func (m *GetTableSpecRequest) XXX_Merge(src proto.Message)

func (*GetTableSpecRequest) XXX_Size 

func (m *GetTableSpecRequest) XXX_Size() int

func (*GetTableSpecRequest) XXX_Unmarshal 

func (m *GetTableSpecRequest) XXX_Unmarshal(b []byte) error

type Image 

type Image struct {
	// Input only. The data representing the image.
	// For Predict calls [image_bytes][google.cloud.automl.v1beta1.Image.image_bytes] must be set, as other options are not
	// currently supported by prediction API. You can read the contents of an
	// uploaded image by using the [content_uri][google.cloud.automl.v1beta1.Image.content_uri] field.
	//
	// Types that are valid to be assigned to Data:
	//	*Image_ImageBytes
	//	*Image_InputConfig
	Data isImage_Data `protobuf_oneof:"data"`
	// Output only. HTTP URI to the thumbnail image.
	ThumbnailUri         string   `protobuf:"bytes,4,opt,name=thumbnail_uri,json=thumbnailUri,proto3" json:"thumbnail_uri,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A representation of an image. Only images up to 30MB in size are supported.

func (*Image) Descriptor 

func (*Image) Descriptor() ([]byte, []int)

func (*Image) GetData 

func (m *Image) GetData() isImage_Data

func (*Image) GetImageBytes 

func (m *Image) GetImageBytes() []byte

func (*Image) GetInputConfig 

func (m *Image) GetInputConfig() *InputConfig

func (*Image) GetThumbnailUri 

func (m *Image) GetThumbnailUri() string

func (*Image) ProtoMessage 

func (*Image) ProtoMessage()

func (*Image) Reset 

func (m *Image) Reset()

func (*Image) String 

func (m *Image) String() string

func (*Image) XXX_DiscardUnknown 

func (m *Image) XXX_DiscardUnknown()

func (*Image) XXX_Marshal 

func (m *Image) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*Image) XXX_Merge 

func (m *Image) XXX_Merge(src proto.Message)

func (*Image) XXX_OneofWrappers 

func (*Image) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*Image) XXX_Size 

func (m *Image) XXX_Size() int

func (*Image) XXX_Unmarshal 

func (m *Image) XXX_Unmarshal(b []byte) error

type ImageClassificationDatasetMetadata 

type ImageClassificationDatasetMetadata struct {
	// Required. Type of the classification problem.
	ClassificationType   ClassificationType `` /* 168-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}           `json:"-"`
	XXX_unrecognized     []byte             `json:"-"`
	XXX_sizecache        int32              `json:"-"`
}

Dataset metadata that is specific to image classification.

func (*ImageClassificationDatasetMetadata) Descriptor 

func (*ImageClassificationDatasetMetadata) Descriptor() ([]byte, []int)

func (*ImageClassificationDatasetMetadata) GetClassificationType 

func (m *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationType

func (*ImageClassificationDatasetMetadata) ProtoMessage 

func (*ImageClassificationDatasetMetadata) ProtoMessage()

func (*ImageClassificationDatasetMetadata) Reset 

func (m *ImageClassificationDatasetMetadata) Reset()

func (*ImageClassificationDatasetMetadata) String 

func (m *ImageClassificationDatasetMetadata) String() string

func (*ImageClassificationDatasetMetadata) XXX_DiscardUnknown 

func (m *ImageClassificationDatasetMetadata) XXX_DiscardUnknown()

func (*ImageClassificationDatasetMetadata) XXX_Marshal 

func (m *ImageClassificationDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImageClassificationDatasetMetadata) XXX_Merge 

func (m *ImageClassificationDatasetMetadata) XXX_Merge(src proto.Message)

func (*ImageClassificationDatasetMetadata) XXX_Size 

func (m *ImageClassificationDatasetMetadata) XXX_Size() int

func (*ImageClassificationDatasetMetadata) XXX_Unmarshal 

func (m *ImageClassificationDatasetMetadata) XXX_Unmarshal(b []byte) error

type ImageClassificationModelDeploymentMetadata 

type ImageClassificationModelDeploymentMetadata struct {
	// Input only. The number of nodes to deploy the model on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the model's
	//
	// [node_qps][google.cloud.automl.v1beta1.ImageClassificationModelMetadata.node_qps].
	// Must be between 1 and 100, inclusive on both ends.
	NodeCount            int64    `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model deployment metadata specific to Image Classification.

func (*ImageClassificationModelDeploymentMetadata) Descriptor 

func (*ImageClassificationModelDeploymentMetadata) Descriptor() ([]byte, []int)

func (*ImageClassificationModelDeploymentMetadata) GetNodeCount 

func (m *ImageClassificationModelDeploymentMetadata) GetNodeCount() int64

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage 

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage()

func (*ImageClassificationModelDeploymentMetadata) Reset 

func (m *ImageClassificationModelDeploymentMetadata) Reset()

func (*ImageClassificationModelDeploymentMetadata) String 

func (m *ImageClassificationModelDeploymentMetadata) String() string

func (*ImageClassificationModelDeploymentMetadata) XXX_DiscardUnknown 

func (m *ImageClassificationModelDeploymentMetadata) XXX_DiscardUnknown()

func (*ImageClassificationModelDeploymentMetadata) XXX_Marshal 

func (m *ImageClassificationModelDeploymentMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImageClassificationModelDeploymentMetadata) XXX_Merge 

func (m *ImageClassificationModelDeploymentMetadata) XXX_Merge(src proto.Message)

func (*ImageClassificationModelDeploymentMetadata) XXX_Size 

func (m *ImageClassificationModelDeploymentMetadata) XXX_Size() int

func (*ImageClassificationModelDeploymentMetadata) XXX_Unmarshal 

func (m *ImageClassificationModelDeploymentMetadata) XXX_Unmarshal(b []byte) error

type ImageClassificationModelMetadata 

type ImageClassificationModelMetadata struct {
	// Optional. The ID of the `base` model. If it is specified, the new model
	// will be created based on the `base` model. Otherwise, the new model will be
	// created from scratch. The `base` model must be in the same
	// `project` and `location` as the new model to create, and have the same
	// `model_type`.
	BaseModelId string `protobuf:"bytes,1,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`
	// Required. The train budget of creating this model, expressed in hours. The
	// actual `train_cost` will be equal or less than this value.
	TrainBudget int64 `protobuf:"varint,2,opt,name=train_budget,json=trainBudget,proto3" json:"train_budget,omitempty"`
	// Output only. The actual train cost of creating this model, expressed in
	// hours. If this model is created from a `base` model, the train cost used
	// to create the `base` model are not included.
	TrainCost int64 `protobuf:"varint,3,opt,name=train_cost,json=trainCost,proto3" json:"train_cost,omitempty"`
	// Output only. The reason that this create model operation stopped,
	// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
	StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`
	// Optional. Type of the model. The available values are:
	// *   `cloud` - Model to be used via prediction calls to AutoML API.
	//               This is the default value.
	// *   `mobile-low-latency-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards. Expected to have low latency, but
	//               may have lower prediction quality than other models.
	// *   `mobile-versatile-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards.
	// *   `mobile-high-accuracy-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards.  Expected to have a higher
	//               latency, but should also have a higher prediction quality
	//               than other models.
	// *   `mobile-core-ml-low-latency-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core
	//               ML afterwards. Expected to have low latency, but may have
	//               lower prediction quality than other models.
	// *   `mobile-core-ml-versatile-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core
	//               ML afterwards.
	// *   `mobile-core-ml-high-accuracy-1` - A model that, in addition to
	//               providing prediction via AutoML API, can also be exported
	//               (see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with
	//               Core ML afterwards.  Expected to have a higher latency, but
	//               should also have a higher prediction quality than other
	//               models.
	ModelType string `protobuf:"bytes,7,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`
	// Output only. An approximate number of online prediction QPS that can
	// be supported by this model per each node on which it is deployed.
	NodeQps float64 `protobuf:"fixed64,13,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`
	// Output only. The number of nodes this model is deployed on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the node_qps field.
	NodeCount            int64    `protobuf:"varint,14,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model metadata for image classification.

func (*ImageClassificationModelMetadata) Descriptor 

func (*ImageClassificationModelMetadata) Descriptor() ([]byte, []int)

func (*ImageClassificationModelMetadata) GetBaseModelId 

func (m *ImageClassificationModelMetadata) GetBaseModelId() string

func (*ImageClassificationModelMetadata) GetModelType 

func (m *ImageClassificationModelMetadata) GetModelType() string

func (*ImageClassificationModelMetadata) GetNodeCount 

func (m *ImageClassificationModelMetadata) GetNodeCount() int64

func (*ImageClassificationModelMetadata) GetNodeQps 

func (m *ImageClassificationModelMetadata) GetNodeQps() float64

func (*ImageClassificationModelMetadata) GetStopReason 

func (m *ImageClassificationModelMetadata) GetStopReason() string

func (*ImageClassificationModelMetadata) GetTrainBudget 

func (m *ImageClassificationModelMetadata) GetTrainBudget() int64

func (*ImageClassificationModelMetadata) GetTrainCost 

func (m *ImageClassificationModelMetadata) GetTrainCost() int64

func (*ImageClassificationModelMetadata) ProtoMessage 

func (*ImageClassificationModelMetadata) ProtoMessage()

func (*ImageClassificationModelMetadata) Reset 

func (m *ImageClassificationModelMetadata) Reset()

func (*ImageClassificationModelMetadata) String 

func (m *ImageClassificationModelMetadata) String() string

func (*ImageClassificationModelMetadata) XXX_DiscardUnknown 

func (m *ImageClassificationModelMetadata) XXX_DiscardUnknown()

func (*ImageClassificationModelMetadata) XXX_Marshal 

func (m *ImageClassificationModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImageClassificationModelMetadata) XXX_Merge 

func (m *ImageClassificationModelMetadata) XXX_Merge(src proto.Message)

func (*ImageClassificationModelMetadata) XXX_Size 

func (m *ImageClassificationModelMetadata) XXX_Size() int

func (*ImageClassificationModelMetadata) XXX_Unmarshal 

func (m *ImageClassificationModelMetadata) XXX_Unmarshal(b []byte) error

type ImageObjectDetectionAnnotation 

type ImageObjectDetectionAnnotation struct {
	// Output only. The rectangle representing the object location.
	BoundingBox *BoundingPoly `protobuf:"bytes,1,opt,name=bounding_box,json=boundingBox,proto3" json:"bounding_box,omitempty"`
	// Output only. The confidence that this annotation is positive for the parent example,
	// value in [0, 1], higher means higher positivity confidence.
	Score                float32  `protobuf:"fixed32,2,opt,name=score,proto3" json:"score,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Annotation details for image object detection.

func (*ImageObjectDetectionAnnotation) Descriptor 

func (*ImageObjectDetectionAnnotation) Descriptor() ([]byte, []int)

func (*ImageObjectDetectionAnnotation) GetBoundingBox 

func (m *ImageObjectDetectionAnnotation) GetBoundingBox() *BoundingPoly

func (*ImageObjectDetectionAnnotation) GetScore 

func (m *ImageObjectDetectionAnnotation) GetScore() float32

func (*ImageObjectDetectionAnnotation) ProtoMessage 

func (*ImageObjectDetectionAnnotation) ProtoMessage()

func (*ImageObjectDetectionAnnotation) Reset 

func (m *ImageObjectDetectionAnnotation) Reset()

func (*ImageObjectDetectionAnnotation) String 

func (m *ImageObjectDetectionAnnotation) String() string

func (*ImageObjectDetectionAnnotation) XXX_DiscardUnknown 

func (m *ImageObjectDetectionAnnotation) XXX_DiscardUnknown()

func (*ImageObjectDetectionAnnotation) XXX_Marshal 

func (m *ImageObjectDetectionAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImageObjectDetectionAnnotation) XXX_Merge 

func (m *ImageObjectDetectionAnnotation) XXX_Merge(src proto.Message)

func (*ImageObjectDetectionAnnotation) XXX_Size 

func (m *ImageObjectDetectionAnnotation) XXX_Size() int

func (*ImageObjectDetectionAnnotation) XXX_Unmarshal 

func (m *ImageObjectDetectionAnnotation) XXX_Unmarshal(b []byte) error

type ImageObjectDetectionDatasetMetadata 

type ImageObjectDetectionDatasetMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Dataset metadata specific to image object detection.

func (*ImageObjectDetectionDatasetMetadata) Descriptor 

func (*ImageObjectDetectionDatasetMetadata) Descriptor() ([]byte, []int)

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage 

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage()

func (*ImageObjectDetectionDatasetMetadata) Reset 

func (m *ImageObjectDetectionDatasetMetadata) Reset()

func (*ImageObjectDetectionDatasetMetadata) String 

func (m *ImageObjectDetectionDatasetMetadata) String() string

func (*ImageObjectDetectionDatasetMetadata) XXX_DiscardUnknown 

func (m *ImageObjectDetectionDatasetMetadata) XXX_DiscardUnknown()

func (*ImageObjectDetectionDatasetMetadata) XXX_Marshal 

func (m *ImageObjectDetectionDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImageObjectDetectionDatasetMetadata) XXX_Merge 

func (m *ImageObjectDetectionDatasetMetadata) XXX_Merge(src proto.Message)

func (*ImageObjectDetectionDatasetMetadata) XXX_Size 

func (m *ImageObjectDetectionDatasetMetadata) XXX_Size() int

func (*ImageObjectDetectionDatasetMetadata) XXX_Unmarshal 

func (m *ImageObjectDetectionDatasetMetadata) XXX_Unmarshal(b []byte) error

type ImageObjectDetectionEvaluationMetrics 

type ImageObjectDetectionEvaluationMetrics struct {
	// Output only. The total number of bounding boxes (i.e. summed over all
	// images) the ground truth used to create this evaluation had.
	EvaluatedBoundingBoxCount int32 `` /* 141-byte string literal not displayed */
	// Output only. The bounding boxes match metrics for each
	// Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
	// and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
	// pair.
	BoundingBoxMetricsEntries []*BoundingBoxMetricsEntry `` /* 140-byte string literal not displayed */
	// Output only. The single metric for bounding boxes evaluation:
	// the mean_average_precision averaged over all bounding_box_metrics_entries.
	BoundingBoxMeanAveragePrecision float32  `` /* 162-byte string literal not displayed */
	XXX_NoUnkeyedLiteral            struct{} `json:"-"`
	XXX_unrecognized                []byte   `json:"-"`
	XXX_sizecache                   int32    `json:"-"`
}

Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.

func (*ImageObjectDetectionEvaluationMetrics) Descriptor 

func (*ImageObjectDetectionEvaluationMetrics) Descriptor() ([]byte, []int)

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision 

func (m *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision() float32

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries 

func (m *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries() []*BoundingBoxMetricsEntry

func (*ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount 

func (m *ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount() int32

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage 

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage()

func (*ImageObjectDetectionEvaluationMetrics) Reset 

func (m *ImageObjectDetectionEvaluationMetrics) Reset()

func (*ImageObjectDetectionEvaluationMetrics) String 

func (m *ImageObjectDetectionEvaluationMetrics) String() string

func (*ImageObjectDetectionEvaluationMetrics) XXX_DiscardUnknown 

func (m *ImageObjectDetectionEvaluationMetrics) XXX_DiscardUnknown()

func (*ImageObjectDetectionEvaluationMetrics) XXX_Marshal 

func (m *ImageObjectDetectionEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImageObjectDetectionEvaluationMetrics) XXX_Merge 

func (m *ImageObjectDetectionEvaluationMetrics) XXX_Merge(src proto.Message)

func (*ImageObjectDetectionEvaluationMetrics) XXX_Size 

func (m *ImageObjectDetectionEvaluationMetrics) XXX_Size() int

func (*ImageObjectDetectionEvaluationMetrics) XXX_Unmarshal 

func (m *ImageObjectDetectionEvaluationMetrics) XXX_Unmarshal(b []byte) error

type ImageObjectDetectionModelDeploymentMetadata 

type ImageObjectDetectionModelDeploymentMetadata struct {
	// Input only. The number of nodes to deploy the model on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the model's
	//
	// [qps_per_node][google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata.qps_per_node].
	// Must be between 1 and 100, inclusive on both ends.
	NodeCount            int64    `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model deployment metadata specific to Image Object Detection.

func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor 

func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor() ([]byte, []int)

func (*ImageObjectDetectionModelDeploymentMetadata) GetNodeCount 

func (m *ImageObjectDetectionModelDeploymentMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage 

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage()

func (*ImageObjectDetectionModelDeploymentMetadata) Reset 

func (m *ImageObjectDetectionModelDeploymentMetadata) Reset()

func (*ImageObjectDetectionModelDeploymentMetadata) String 

func (m *ImageObjectDetectionModelDeploymentMetadata) String() string

func (*ImageObjectDetectionModelDeploymentMetadata) XXX_DiscardUnknown 

func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_DiscardUnknown()

func (*ImageObjectDetectionModelDeploymentMetadata) XXX_Marshal 

func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImageObjectDetectionModelDeploymentMetadata) XXX_Merge 

func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Merge(src proto.Message)

func (*ImageObjectDetectionModelDeploymentMetadata) XXX_Size 

func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Size() int

func (*ImageObjectDetectionModelDeploymentMetadata) XXX_Unmarshal 

func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Unmarshal(b []byte) error

type ImageObjectDetectionModelMetadata 

type ImageObjectDetectionModelMetadata struct {
	// Optional. Type of the model. The available values are:
	// *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
	//               calls to AutoML API. Expected to have a higher latency, but
	//               should also have a higher prediction quality than other
	//               models.
	// *   `cloud-low-latency-1` -  A model to be used via prediction
	//               calls to AutoML API. Expected to have low latency, but may
	//               have lower prediction quality than other models.
	// *   `mobile-low-latency-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards. Expected to have low latency, but
	//               may have lower prediction quality than other models.
	// *   `mobile-versatile-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards.
	// *   `mobile-high-accuracy-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards.  Expected to have a higher
	//               latency, but should also have a higher prediction quality
	//               than other models.
	ModelType string `protobuf:"bytes,1,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`
	// Output only. The number of nodes this model is deployed on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the qps_per_node field.
	NodeCount int64 `protobuf:"varint,3,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	// Output only. An approximate number of online prediction QPS that can
	// be supported by this model per each node on which it is deployed.
	NodeQps float64 `protobuf:"fixed64,4,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`
	// Output only. The reason that this create model operation stopped,
	// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
	StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`
	// The train budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour. The actual
	// `train_cost` will be equal or less than this value. If further model
	// training ceases to provide any improvements, it will stop without using
	// full budget and the stop_reason will be `MODEL_CONVERGED`.
	// Note, node_hour  = actual_hour * number_of_nodes_invovled.
	// For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
	// the train budget must be between 20,000 and 900,000 milli node hours,
	// inclusive. The default value is 216, 000 which represents one day in
	// wall time.
	// For model type `mobile-low-latency-1`, `mobile-versatile-1`,
	// `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
	// `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
	// budget must be between 1,000 and 100,000 milli node hours, inclusive.
	// The default value is 24, 000 which represents one day in wall time.
	TrainBudgetMilliNodeHours int64 `` /* 143-byte string literal not displayed */
	// Output only. The actual train cost of creating this model, expressed in
	// milli node hours, i.e. 1,000 value in this field means 1 node hour.
	// Guaranteed to not exceed the train budget.
	TrainCostMilliNodeHours int64    `` /* 137-byte string literal not displayed */
	XXX_NoUnkeyedLiteral    struct{} `json:"-"`
	XXX_unrecognized        []byte   `json:"-"`
	XXX_sizecache           int32    `json:"-"`
}

Model metadata specific to image object detection.

func (*ImageObjectDetectionModelMetadata) Descriptor 

func (*ImageObjectDetectionModelMetadata) Descriptor() ([]byte, []int)

func (*ImageObjectDetectionModelMetadata) GetModelType 

func (m *ImageObjectDetectionModelMetadata) GetModelType() string

func (*ImageObjectDetectionModelMetadata) GetNodeCount 

func (m *ImageObjectDetectionModelMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelMetadata) GetNodeQps 

func (m *ImageObjectDetectionModelMetadata) GetNodeQps() float64

func (*ImageObjectDetectionModelMetadata) GetStopReason 

func (m *ImageObjectDetectionModelMetadata) GetStopReason() string

func (*ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours 

func (m *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours 

func (m *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) ProtoMessage 

func (*ImageObjectDetectionModelMetadata) ProtoMessage()

func (*ImageObjectDetectionModelMetadata) Reset 

func (m *ImageObjectDetectionModelMetadata) Reset()

func (*ImageObjectDetectionModelMetadata) String 

func (m *ImageObjectDetectionModelMetadata) String() string

func (*ImageObjectDetectionModelMetadata) XXX_DiscardUnknown 

func (m *ImageObjectDetectionModelMetadata) XXX_DiscardUnknown()

func (*ImageObjectDetectionModelMetadata) XXX_Marshal 

func (m *ImageObjectDetectionModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImageObjectDetectionModelMetadata) XXX_Merge 

func (m *ImageObjectDetectionModelMetadata) XXX_Merge(src proto.Message)

func (*ImageObjectDetectionModelMetadata) XXX_Size 

func (m *ImageObjectDetectionModelMetadata) XXX_Size() int

func (*ImageObjectDetectionModelMetadata) XXX_Unmarshal 

func (m *ImageObjectDetectionModelMetadata) XXX_Unmarshal(b []byte) error

type Image_ImageBytes 

type Image_ImageBytes struct {
	ImageBytes []byte `protobuf:"bytes,1,opt,name=image_bytes,json=imageBytes,proto3,oneof"`
}

type Image_InputConfig 

type Image_InputConfig struct {
	InputConfig *InputConfig `protobuf:"bytes,6,opt,name=input_config,json=inputConfig,proto3,oneof"`
}

type ImportDataOperationMetadata 

type ImportDataOperationMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Details of ImportData operation.

func (*ImportDataOperationMetadata) Descriptor 

func (*ImportDataOperationMetadata) Descriptor() ([]byte, []int)

func (*ImportDataOperationMetadata) ProtoMessage 

func (*ImportDataOperationMetadata) ProtoMessage()

func (*ImportDataOperationMetadata) Reset 

func (m *ImportDataOperationMetadata) Reset()

func (*ImportDataOperationMetadata) String 

func (m *ImportDataOperationMetadata) String() string

func (*ImportDataOperationMetadata) XXX_DiscardUnknown 

func (m *ImportDataOperationMetadata) XXX_DiscardUnknown()

func (*ImportDataOperationMetadata) XXX_Marshal 

func (m *ImportDataOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImportDataOperationMetadata) XXX_Merge 

func (m *ImportDataOperationMetadata) XXX_Merge(src proto.Message)

func (*ImportDataOperationMetadata) XXX_Size 

func (m *ImportDataOperationMetadata) XXX_Size() int

func (*ImportDataOperationMetadata) XXX_Unmarshal 

func (m *ImportDataOperationMetadata) XXX_Unmarshal(b []byte) error

type ImportDataRequest 

type ImportDataRequest struct {
	// Required. Dataset name. Dataset must already exist. All imported
	// annotations and examples will be added.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired input location and its domain specific semantics,
	// if any.
	InputConfig          *InputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	XXX_NoUnkeyedLiteral struct{}     `json:"-"`
	XXX_unrecognized     []byte       `json:"-"`
	XXX_sizecache        int32        `json:"-"`
}

Request message for [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData].

func (*ImportDataRequest) Descriptor 

func (*ImportDataRequest) Descriptor() ([]byte, []int)

func (*ImportDataRequest) GetInputConfig 

func (m *ImportDataRequest) GetInputConfig() *InputConfig

func (*ImportDataRequest) GetName 

func (m *ImportDataRequest) GetName() string

func (*ImportDataRequest) ProtoMessage 

func (*ImportDataRequest) ProtoMessage()

func (*ImportDataRequest) Reset 

func (m *ImportDataRequest) Reset()

func (*ImportDataRequest) String 

func (m *ImportDataRequest) String() string

func (*ImportDataRequest) XXX_DiscardUnknown 

func (m *ImportDataRequest) XXX_DiscardUnknown()

func (*ImportDataRequest) XXX_Marshal 

func (m *ImportDataRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ImportDataRequest) XXX_Merge 

func (m *ImportDataRequest) XXX_Merge(src proto.Message)

func (*ImportDataRequest) XXX_Size 

func (m *ImportDataRequest) XXX_Size() int

func (*ImportDataRequest) XXX_Unmarshal 

func (m *ImportDataRequest) XXX_Unmarshal(b []byte) error

type InputConfig 

type InputConfig struct {
	// The source of the input.
	//
	// Types that are valid to be assigned to Source:
	//	*InputConfig_GcsSource
	//	*InputConfig_BigquerySource
	Source isInputConfig_Source `protobuf_oneof:"source"`
	// Additional domain-specific parameters describing the semantic of the
	// imported data, any string must be up to 25000
	// characters long.
	//
	// *  For Tables:
	//    `schema_inference_version` - (integer) Required. The version of the
	//        algorithm that should be used for the initial inference of the
	//        schema (columns' DataTypes) of the table the data is being imported
	//        into. Allowed values: "1".
	Params               map[string]string `` /* 153-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}          `json:"-"`
	XXX_unrecognized     []byte            `json:"-"`
	XXX_sizecache        int32             `json:"-"`
}

Input configuration for ImportData Action.

The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] is expected, unless specified otherwise. Additionally any input .CSV file by itself must be 100MB or smaller, unless specified otherwise. If an "example" file (that is, image, video etc.) with identical content (even if it had different GCS_FILE_PATH) is mentioned multiple times, then its label, bounding boxes etc. are appended. The same file should be always provided with the same ML_USE and GCS_FILE_PATH, if it is not, then these values are nondeterministically selected from the given ones.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

  • For Image Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO For MULTICLASS classification type, at most one LABEL is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no LABEL. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy TEST,gs://folder/image2.jpg,dandelion,tulip,rose UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg

  • For Image Object Detection: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,) GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined per line). If an image has not yet been labeled, then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. For images which are known to not contain any bounding boxes, they should be labelled explictly as "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the BOUNDING_BOX. Sample rows: TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 TEST,gs://folder/im3.png,,,,,,,,, TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,

  • For Video Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have given ML_USE, using the following row format: GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it shuold be mentioned just once with ",," in place of LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,120,180.000021 gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,,

  • For Video Object Tracking: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have given ML_USE, using one of the following row format: GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or GCS_FILE_PATH,,,,,,,,,, Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing INSTANCE_IDs can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same INSTANCE_ID is given to it. TIMESTAMP must be within the length of the video, the BOUNDING_BOX is assumed to be drawn on the closest video's frame to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place of LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, gs://folder/video2.avi,,,,,,,,,,,

  • For Text Extraction: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which either imports text in-line or as documents. Any given .JSONL file must be 100MB or smaller. The in-line .JSONL file contains, per line, a proto that wraps a TextSnippet proto (in json representation) followed by one or more AnnotationPayload protos (called annotations), which have display_name and text_extraction detail populated. The given text is expected to be annotated exhaustively, for example, if you look for animals and text contains "dolphin" that is not labeled, then "dolphin" is assumed to not be an animal. Any given text snippet content must be 10KB or smaller, and also be UTF-8 NFC encoded (ASCII already is). The document .JSONL file contains, per line, a proto that wraps a Document proto. The Document proto must have either document_text or input_config set. In document_text case, the Document proto may also contain the spatial information of the document, including layout, document dimension and page number. In input_config case, only PDF documents are supported now, and each document may be up to 2MB large. Currently, annotations on documents cannot be specified at import. Three sample CSV rows: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl Sample in-line JSON Lines file for entity extraction (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "document_text": {"content": "dog cat"} "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 3, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ], }, "text_segment_type": TOKEN, }, { "text_segment": { "start_offset": 4, "end_offset": 7, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.4, "y": 0.1}, {"x": 0.4, "y": 0.3}, {"x": 0.8, "y": 0.3}, {"x": 0.8, "y": 0.1}, ], }, "text_segment_type": TOKEN, }

    ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 1, }, "annotations": [ { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 0, "end_offset": 3}} }, { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 4, "end_offset": 7}} } ], }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 8} } } ] } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ] } } } }

  • For Text Classification: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, i.e. prefixed by "gs://", it will be treated as a GCS_FILE_PATH, else if the content is enclosed within double quotes (""), it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content excluding quotes is treated as to be imported text snippet. In both cases, the text snippet/file size must be within 128kB. Maximum 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood TRAIN,gs://folder/content.txt,SlowService TEST,"Typically always bad service there.",RudeService VALIDATE,"Stomach ache to go.",BadFood

  • For Text Sentiment: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content itself is treated as to be imported text snippet. In both cases, the text snippet must be up to 500 characters long. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 TRAIN,"I need this product so bad",3 TEST,"Thank you for this product.",4 VALIDATE,gs://folder/content.txt,2

  • For Tables: Either [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or

[bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source] 

can be used. All inputs is concatenated into a single

[primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name] 

For gcs_source:
  CSV file(s), where the first row of the first file is the header,
  containing unique column names. If the first row of a subsequent
  file is the same as the header, then it is also treated as a
  header. All other rows contain values for the corresponding
  columns.
  Each .CSV file by itself must be 10GB or smaller, and their total
  size must be 100GB or smaller.
  First three sample rows of a CSV file:
  "Id","First Name","Last Name","Dob","Addresses"

"1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"

"2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} 

       For bigquery_source:
         An URI of a BigQuery table. The user data size of the BigQuery
         table must be 100GB or smaller.
       An imported table must have between 2 and 1,000 columns, inclusive,
       and between 1000 and 100,000,000 rows, inclusive. There are at most 5
       import data running in parallel.
Definitions:
ML_USE = "TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED"
         Describes how the given example (file) should be used for model
         training. "UNASSIGNED" can be used when user has no preference.
GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/image1.png".
LABEL = A display name of an object on an image, video etc., e.g. "dog".
        Must be up to 32 characters long and can consist only of ASCII
        Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
        For each label an AnnotationSpec is created which display_name
        becomes the label; AnnotationSpecs are given back in predictions.
INSTANCE_ID = A positive integer that identifies a specific instance of a
              labeled entity on an example. Used e.g. to track two cars on
              a video while being able to tell apart which one is which.
BOUNDING_BOX = VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,
               A rectangle parallel to the frame of the example (image,
               video). If 4 vertices are given they are connected by edges
               in the order provided, if 2 are given they are recognized
               as diagonally opposite vertices of the rectangle.
VERTEX = COORDINATE,COORDINATE
         First coordinate is horizontal (x), the second is vertical (y).
COORDINATE = A float in 0 to 1 range, relative to total length of
             image or video in given dimension. For fractions the
             leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
             Point 0,0 is in top left.
TIME_SEGMENT_START = TIME_OFFSET
                     Expresses a beginning, inclusive, of a time segment
                     within an example that has a time dimension
                     (e.g. video).
TIME_SEGMENT_END = TIME_OFFSET
                   Expresses an end, exclusive, of a time segment within
                   an example that has a time dimension (e.g. video).
TIME_OFFSET = A number of seconds as measured from the start of an
              example (e.g. video). Fractions are allowed, up to a
              microsecond precision. "inf" is allowed, and it means the end
              of the example.
TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
               double quotes ("").
SENTIMENT = An integer between 0 and
            Dataset.text_sentiment_dataset_metadata.sentiment_max
            (inclusive). Describes the ordinal of the sentiment - higher
            value means a more positive sentiment. All the values are
            completely relative, i.e. neither 0 needs to mean a negative or
            neutral sentiment nor sentiment_max needs to mean a positive one
            - it is just required that 0 is the least positive sentiment
            in the data, and sentiment_max is the  most positive one.
            The SENTIMENT shouldn't be confused with "score" or "magnitude"
            from the previous Natural Language Sentiment Analysis API.
            All SENTIMENT values between 0 and sentiment_max must be
            represented in the imported data. On prediction the same 0 to
            sentiment_max range will be used. The difference between
            neighboring sentiment values needs not to be uniform, e.g. 1 and
            2 may be similar whereas the difference between 2 and 3 may be
            huge.

Errors:
If any of the provided CSV files can't be parsed or if more than certain
percent of CSV rows cannot be processed then the operation fails and
nothing is imported. Regardless of overall success or failure the per-row
failures, up to a certain count cap, is listed in
Operation.metadata.partial_failures.

func (*InputConfig) Descriptor 

func (*InputConfig) Descriptor() ([]byte, []int)

func (*InputConfig) GetBigquerySource 

func (m *InputConfig) GetBigquerySource() *BigQuerySource

func (*InputConfig) GetGcsSource 

func (m *InputConfig) GetGcsSource() *GcsSource

func (*InputConfig) GetParams 

func (m *InputConfig) GetParams() map[string]string

func (*InputConfig) GetSource 

func (m *InputConfig) GetSource() isInputConfig_Source

func (*InputConfig) ProtoMessage 

func (*InputConfig) ProtoMessage()

func (*InputConfig) Reset 

func (m *InputConfig) Reset()

func (*InputConfig) String 

func (m *InputConfig) String() string

func (*InputConfig) XXX_DiscardUnknown 

func (m *InputConfig) XXX_DiscardUnknown()

func (*InputConfig) XXX_Marshal 

func (m *InputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*InputConfig) XXX_Merge 

func (m *InputConfig) XXX_Merge(src proto.Message)

func (*InputConfig) XXX_OneofWrappers 

func (*InputConfig) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*InputConfig) XXX_Size 

func (m *InputConfig) XXX_Size() int

func (*InputConfig) XXX_Unmarshal 

func (m *InputConfig) XXX_Unmarshal(b []byte) error

type InputConfig_BigquerySource 

type InputConfig_BigquerySource struct {
	BigquerySource *BigQuerySource `protobuf:"bytes,3,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}

type InputConfig_GcsSource 

type InputConfig_GcsSource struct {
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

type ListColumnSpecsRequest 

type ListColumnSpecsRequest struct {
	// Required. The resource name of the table spec to list column specs from.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Mask specifying which fields to read.
	FieldMask *field_mask.FieldMask `protobuf:"bytes,2,opt,name=field_mask,json=fieldMask,proto3" json:"field_mask,omitempty"`
	// Filter expression, see go/filtering.
	Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
	// Requested page size. The server can return fewer results than requested.
	// If unspecified, the server will pick a default size.
	PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
	// A token identifying a page of results for the server to return.
	// Typically obtained from the
	// [ListColumnSpecsResponse.next_page_token][google.cloud.automl.v1beta1.ListColumnSpecsResponse.next_page_token] field of the previous
	// [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs] call.
	PageToken            string   `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].

func (*ListColumnSpecsRequest) Descriptor 

func (*ListColumnSpecsRequest) Descriptor() ([]byte, []int)

func (*ListColumnSpecsRequest) GetFieldMask 

func (m *ListColumnSpecsRequest) GetFieldMask() *field_mask.FieldMask

func (*ListColumnSpecsRequest) GetFilter 

func (m *ListColumnSpecsRequest) GetFilter() string

func (*ListColumnSpecsRequest) GetPageSize 

func (m *ListColumnSpecsRequest) GetPageSize() int32

func (*ListColumnSpecsRequest) GetPageToken 

func (m *ListColumnSpecsRequest) GetPageToken() string

func (*ListColumnSpecsRequest) GetParent 

func (m *ListColumnSpecsRequest) GetParent() string

func (*ListColumnSpecsRequest) ProtoMessage 

func (*ListColumnSpecsRequest) ProtoMessage()

func (*ListColumnSpecsRequest) Reset 

func (m *ListColumnSpecsRequest) Reset()

func (*ListColumnSpecsRequest) String 

func (m *ListColumnSpecsRequest) String() string

func (*ListColumnSpecsRequest) XXX_DiscardUnknown 

func (m *ListColumnSpecsRequest) XXX_DiscardUnknown()

func (*ListColumnSpecsRequest) XXX_Marshal 

func (m *ListColumnSpecsRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListColumnSpecsRequest) XXX_Merge 

func (m *ListColumnSpecsRequest) XXX_Merge(src proto.Message)

func (*ListColumnSpecsRequest) XXX_Size 

func (m *ListColumnSpecsRequest) XXX_Size() int

func (*ListColumnSpecsRequest) XXX_Unmarshal 

func (m *ListColumnSpecsRequest) XXX_Unmarshal(b []byte) error

type ListColumnSpecsResponse 

type ListColumnSpecsResponse struct {
	// The column specs read.
	ColumnSpecs []*ColumnSpec `protobuf:"bytes,1,rep,name=column_specs,json=columnSpecs,proto3" json:"column_specs,omitempty"`
	// A token to retrieve next page of results.
	// Pass to [ListColumnSpecsRequest.page_token][google.cloud.automl.v1beta1.ListColumnSpecsRequest.page_token] to obtain that page.
	NextPageToken        string   `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Response message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].

func (*ListColumnSpecsResponse) Descriptor 

func (*ListColumnSpecsResponse) Descriptor() ([]byte, []int)

func (*ListColumnSpecsResponse) GetColumnSpecs 

func (m *ListColumnSpecsResponse) GetColumnSpecs() []*ColumnSpec

func (*ListColumnSpecsResponse) GetNextPageToken 

func (m *ListColumnSpecsResponse) GetNextPageToken() string

func (*ListColumnSpecsResponse) ProtoMessage 

func (*ListColumnSpecsResponse) ProtoMessage()

func (*ListColumnSpecsResponse) Reset 

func (m *ListColumnSpecsResponse) Reset()

func (*ListColumnSpecsResponse) String 

func (m *ListColumnSpecsResponse) String() string

func (*ListColumnSpecsResponse) XXX_DiscardUnknown 

func (m *ListColumnSpecsResponse) XXX_DiscardUnknown()

func (*ListColumnSpecsResponse) XXX_Marshal 

func (m *ListColumnSpecsResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListColumnSpecsResponse) XXX_Merge 

func (m *ListColumnSpecsResponse) XXX_Merge(src proto.Message)

func (*ListColumnSpecsResponse) XXX_Size 

func (m *ListColumnSpecsResponse) XXX_Size() int

func (*ListColumnSpecsResponse) XXX_Unmarshal 

func (m *ListColumnSpecsResponse) XXX_Unmarshal(b []byte) error

type ListDatasetsRequest 

type ListDatasetsRequest struct {
	// Required. The resource name of the project from which to list datasets.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// An expression for filtering the results of the request.
	//
	//   * `dataset_metadata` - for existence of the case (e.g.
	//             image_classification_dataset_metadata:*). Some examples of using the filter are:
	//
	//   * `translation_dataset_metadata:*` --> The dataset has
	//                                          translation_dataset_metadata.
	Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
	// Requested page size. Server may return fewer results than requested.
	// If unspecified, server will pick a default size.
	PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
	// A token identifying a page of results for the server to return
	// Typically obtained via
	// [ListDatasetsResponse.next_page_token][google.cloud.automl.v1beta1.ListDatasetsResponse.next_page_token] of the previous
	// [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets] call.
	PageToken            string   `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].

func (*ListDatasetsRequest) Descriptor 

func (*ListDatasetsRequest) Descriptor() ([]byte, []int)

func (*ListDatasetsRequest) GetFilter 

func (m *ListDatasetsRequest) GetFilter() string

func (*ListDatasetsRequest) GetPageSize 

func (m *ListDatasetsRequest) GetPageSize() int32

func (*ListDatasetsRequest) GetPageToken 

func (m *ListDatasetsRequest) GetPageToken() string

func (*ListDatasetsRequest) GetParent 

func (m *ListDatasetsRequest) GetParent() string

func (*ListDatasetsRequest) ProtoMessage 

func (*ListDatasetsRequest) ProtoMessage()

func (*ListDatasetsRequest) Reset 

func (m *ListDatasetsRequest) Reset()

func (*ListDatasetsRequest) String 

func (m *ListDatasetsRequest) String() string

func (*ListDatasetsRequest) XXX_DiscardUnknown 

func (m *ListDatasetsRequest) XXX_DiscardUnknown()

func (*ListDatasetsRequest) XXX_Marshal 

func (m *ListDatasetsRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListDatasetsRequest) XXX_Merge 

func (m *ListDatasetsRequest) XXX_Merge(src proto.Message)

func (*ListDatasetsRequest) XXX_Size 

func (m *ListDatasetsRequest) XXX_Size() int

func (*ListDatasetsRequest) XXX_Unmarshal 

func (m *ListDatasetsRequest) XXX_Unmarshal(b []byte) error

type ListDatasetsResponse 

type ListDatasetsResponse struct {
	// The datasets read.
	Datasets []*Dataset `protobuf:"bytes,1,rep,name=datasets,proto3" json:"datasets,omitempty"`
	// A token to retrieve next page of results.
	// Pass to [ListDatasetsRequest.page_token][google.cloud.automl.v1beta1.ListDatasetsRequest.page_token] to obtain that page.
	NextPageToken        string   `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Response message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].

func (*ListDatasetsResponse) Descriptor 

func (*ListDatasetsResponse) Descriptor() ([]byte, []int)

func (*ListDatasetsResponse) GetDatasets 

func (m *ListDatasetsResponse) GetDatasets() []*Dataset

func (*ListDatasetsResponse) GetNextPageToken 

func (m *ListDatasetsResponse) GetNextPageToken() string

func (*ListDatasetsResponse) ProtoMessage 

func (*ListDatasetsResponse) ProtoMessage()

func (*ListDatasetsResponse) Reset 

func (m *ListDatasetsResponse) Reset()

func (*ListDatasetsResponse) String 

func (m *ListDatasetsResponse) String() string

func (*ListDatasetsResponse) XXX_DiscardUnknown 

func (m *ListDatasetsResponse) XXX_DiscardUnknown()

func (*ListDatasetsResponse) XXX_Marshal 

func (m *ListDatasetsResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListDatasetsResponse) XXX_Merge 

func (m *ListDatasetsResponse) XXX_Merge(src proto.Message)

func (*ListDatasetsResponse) XXX_Size 

func (m *ListDatasetsResponse) XXX_Size() int

func (*ListDatasetsResponse) XXX_Unmarshal 

func (m *ListDatasetsResponse) XXX_Unmarshal(b []byte) error

type ListModelEvaluationsRequest 

type ListModelEvaluationsRequest struct {
	// Required. Resource name of the model to list the model evaluations for.
	// If modelId is set as "-", this will list model evaluations from across all
	// models of the parent location.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// An expression for filtering the results of the request.
	//
	//   * `annotation_spec_id` - for =, !=  or existence. See example below for
	//                          the last.
	//
	// Some examples of using the filter are:
	//
	//   * `annotation_spec_id!=4` --> The model evaluation was done for
	//                             annotation spec with ID different than 4.
	//   * `NOT annotation_spec_id:*` --> The model evaluation was done for
	//                                aggregate of all annotation specs.
	Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
	// Requested page size.
	PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
	// A token identifying a page of results for the server to return.
	// Typically obtained via
	// [ListModelEvaluationsResponse.next_page_token][google.cloud.automl.v1beta1.ListModelEvaluationsResponse.next_page_token] of the previous
	// [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations] call.
	PageToken            string   `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].

func (*ListModelEvaluationsRequest) Descriptor 

func (*ListModelEvaluationsRequest) Descriptor() ([]byte, []int)

func (*ListModelEvaluationsRequest) GetFilter 

func (m *ListModelEvaluationsRequest) GetFilter() string

func (*ListModelEvaluationsRequest) GetPageSize 

func (m *ListModelEvaluationsRequest) GetPageSize() int32

func (*ListModelEvaluationsRequest) GetPageToken 

func (m *ListModelEvaluationsRequest) GetPageToken() string

func (*ListModelEvaluationsRequest) GetParent 

func (m *ListModelEvaluationsRequest) GetParent() string

func (*ListModelEvaluationsRequest) ProtoMessage 

func (*ListModelEvaluationsRequest) ProtoMessage()

func (*ListModelEvaluationsRequest) Reset 

func (m *ListModelEvaluationsRequest) Reset()

func (*ListModelEvaluationsRequest) String 

func (m *ListModelEvaluationsRequest) String() string

func (*ListModelEvaluationsRequest) XXX_DiscardUnknown 

func (m *ListModelEvaluationsRequest) XXX_DiscardUnknown()

func (*ListModelEvaluationsRequest) XXX_Marshal 

func (m *ListModelEvaluationsRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListModelEvaluationsRequest) XXX_Merge 

func (m *ListModelEvaluationsRequest) XXX_Merge(src proto.Message)

func (*ListModelEvaluationsRequest) XXX_Size 

func (m *ListModelEvaluationsRequest) XXX_Size() int

func (*ListModelEvaluationsRequest) XXX_Unmarshal 

func (m *ListModelEvaluationsRequest) XXX_Unmarshal(b []byte) error

type ListModelEvaluationsResponse 

type ListModelEvaluationsResponse struct {
	// List of model evaluations in the requested page.
	ModelEvaluation []*ModelEvaluation `protobuf:"bytes,1,rep,name=model_evaluation,json=modelEvaluation,proto3" json:"model_evaluation,omitempty"`
	// A token to retrieve next page of results.
	// Pass to the [ListModelEvaluationsRequest.page_token][google.cloud.automl.v1beta1.ListModelEvaluationsRequest.page_token] field of a new
	// [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations] request to obtain that page.
	NextPageToken        string   `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].

func (*ListModelEvaluationsResponse) Descriptor 

func (*ListModelEvaluationsResponse) Descriptor() ([]byte, []int)

func (*ListModelEvaluationsResponse) GetModelEvaluation 

func (m *ListModelEvaluationsResponse) GetModelEvaluation() []*ModelEvaluation

func (*ListModelEvaluationsResponse) GetNextPageToken 

func (m *ListModelEvaluationsResponse) GetNextPageToken() string

func (*ListModelEvaluationsResponse) ProtoMessage 

func (*ListModelEvaluationsResponse) ProtoMessage()

func (*ListModelEvaluationsResponse) Reset 

func (m *ListModelEvaluationsResponse) Reset()

func (*ListModelEvaluationsResponse) String 

func (m *ListModelEvaluationsResponse) String() string

func (*ListModelEvaluationsResponse) XXX_DiscardUnknown 

func (m *ListModelEvaluationsResponse) XXX_DiscardUnknown()

func (*ListModelEvaluationsResponse) XXX_Marshal 

func (m *ListModelEvaluationsResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListModelEvaluationsResponse) XXX_Merge 

func (m *ListModelEvaluationsResponse) XXX_Merge(src proto.Message)

func (*ListModelEvaluationsResponse) XXX_Size 

func (m *ListModelEvaluationsResponse) XXX_Size() int

func (*ListModelEvaluationsResponse) XXX_Unmarshal 

func (m *ListModelEvaluationsResponse) XXX_Unmarshal(b []byte) error

type ListModelsRequest 

type ListModelsRequest struct {
	// Required. Resource name of the project, from which to list the models.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// An expression for filtering the results of the request.
	//
	//   * `model_metadata` - for existence of the case (e.g.
	//             video_classification_model_metadata:*).
	//   * `dataset_id` - for = or !=. Some examples of using the filter are:
	//
	//   * `image_classification_model_metadata:*` --> The model has
	//                                        image_classification_model_metadata.
	//   * `dataset_id=5` --> The model was created from a dataset with ID 5.
	Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
	// Requested page size.
	PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
	// A token identifying a page of results for the server to return
	// Typically obtained via
	// [ListModelsResponse.next_page_token][google.cloud.automl.v1beta1.ListModelsResponse.next_page_token] of the previous
	// [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels] call.
	PageToken            string   `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].

func (*ListModelsRequest) Descriptor 

func (*ListModelsRequest) Descriptor() ([]byte, []int)

func (*ListModelsRequest) GetFilter 

func (m *ListModelsRequest) GetFilter() string

func (*ListModelsRequest) GetPageSize 

func (m *ListModelsRequest) GetPageSize() int32

func (*ListModelsRequest) GetPageToken 

func (m *ListModelsRequest) GetPageToken() string

func (*ListModelsRequest) GetParent 

func (m *ListModelsRequest) GetParent() string

func (*ListModelsRequest) ProtoMessage 

func (*ListModelsRequest) ProtoMessage()

func (*ListModelsRequest) Reset 

func (m *ListModelsRequest) Reset()

func (*ListModelsRequest) String 

func (m *ListModelsRequest) String() string

func (*ListModelsRequest) XXX_DiscardUnknown 

func (m *ListModelsRequest) XXX_DiscardUnknown()

func (*ListModelsRequest) XXX_Marshal 

func (m *ListModelsRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListModelsRequest) XXX_Merge 

func (m *ListModelsRequest) XXX_Merge(src proto.Message)

func (*ListModelsRequest) XXX_Size 

func (m *ListModelsRequest) XXX_Size() int

func (*ListModelsRequest) XXX_Unmarshal 

func (m *ListModelsRequest) XXX_Unmarshal(b []byte) error

type ListModelsResponse 

type ListModelsResponse struct {
	// List of models in the requested page.
	Model []*Model `protobuf:"bytes,1,rep,name=model,proto3" json:"model,omitempty"`
	// A token to retrieve next page of results.
	// Pass to [ListModelsRequest.page_token][google.cloud.automl.v1beta1.ListModelsRequest.page_token] to obtain that page.
	NextPageToken        string   `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Response message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].

func (*ListModelsResponse) Descriptor 

func (*ListModelsResponse) Descriptor() ([]byte, []int)

func (*ListModelsResponse) GetModel 

func (m *ListModelsResponse) GetModel() []*Model

func (*ListModelsResponse) GetNextPageToken 

func (m *ListModelsResponse) GetNextPageToken() string

func (*ListModelsResponse) ProtoMessage 

func (*ListModelsResponse) ProtoMessage()

func (*ListModelsResponse) Reset 

func (m *ListModelsResponse) Reset()

func (*ListModelsResponse) String 

func (m *ListModelsResponse) String() string

func (*ListModelsResponse) XXX_DiscardUnknown 

func (m *ListModelsResponse) XXX_DiscardUnknown()

func (*ListModelsResponse) XXX_Marshal 

func (m *ListModelsResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListModelsResponse) XXX_Merge 

func (m *ListModelsResponse) XXX_Merge(src proto.Message)

func (*ListModelsResponse) XXX_Size 

func (m *ListModelsResponse) XXX_Size() int

func (*ListModelsResponse) XXX_Unmarshal 

func (m *ListModelsResponse) XXX_Unmarshal(b []byte) error

type ListTableSpecsRequest 

type ListTableSpecsRequest struct {
	// Required. The resource name of the dataset to list table specs from.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Mask specifying which fields to read.
	FieldMask *field_mask.FieldMask `protobuf:"bytes,2,opt,name=field_mask,json=fieldMask,proto3" json:"field_mask,omitempty"`
	// Filter expression, see go/filtering.
	Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
	// Requested page size. The server can return fewer results than requested.
	// If unspecified, the server will pick a default size.
	PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
	// A token identifying a page of results for the server to return.
	// Typically obtained from the
	// [ListTableSpecsResponse.next_page_token][google.cloud.automl.v1beta1.ListTableSpecsResponse.next_page_token] field of the previous
	// [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs] call.
	PageToken            string   `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].

func (*ListTableSpecsRequest) Descriptor 

func (*ListTableSpecsRequest) Descriptor() ([]byte, []int)

func (*ListTableSpecsRequest) GetFieldMask 

func (m *ListTableSpecsRequest) GetFieldMask() *field_mask.FieldMask

func (*ListTableSpecsRequest) GetFilter 

func (m *ListTableSpecsRequest) GetFilter() string

func (*ListTableSpecsRequest) GetPageSize 

func (m *ListTableSpecsRequest) GetPageSize() int32

func (*ListTableSpecsRequest) GetPageToken 

func (m *ListTableSpecsRequest) GetPageToken() string

func (*ListTableSpecsRequest) GetParent 

func (m *ListTableSpecsRequest) GetParent() string

func (*ListTableSpecsRequest) ProtoMessage 

func (*ListTableSpecsRequest) ProtoMessage()

func (*ListTableSpecsRequest) Reset 

func (m *ListTableSpecsRequest) Reset()

func (*ListTableSpecsRequest) String 

func (m *ListTableSpecsRequest) String() string

func (*ListTableSpecsRequest) XXX_DiscardUnknown 

func (m *ListTableSpecsRequest) XXX_DiscardUnknown()

func (*ListTableSpecsRequest) XXX_Marshal 

func (m *ListTableSpecsRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListTableSpecsRequest) XXX_Merge 

func (m *ListTableSpecsRequest) XXX_Merge(src proto.Message)

func (*ListTableSpecsRequest) XXX_Size 

func (m *ListTableSpecsRequest) XXX_Size() int

func (*ListTableSpecsRequest) XXX_Unmarshal 

func (m *ListTableSpecsRequest) XXX_Unmarshal(b []byte) error

type ListTableSpecsResponse 

type ListTableSpecsResponse struct {
	// The table specs read.
	TableSpecs []*TableSpec `protobuf:"bytes,1,rep,name=table_specs,json=tableSpecs,proto3" json:"table_specs,omitempty"`
	// A token to retrieve next page of results.
	// Pass to [ListTableSpecsRequest.page_token][google.cloud.automl.v1beta1.ListTableSpecsRequest.page_token] to obtain that page.
	NextPageToken        string   `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Response message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].

func (*ListTableSpecsResponse) Descriptor 

func (*ListTableSpecsResponse) Descriptor() ([]byte, []int)

func (*ListTableSpecsResponse) GetNextPageToken 

func (m *ListTableSpecsResponse) GetNextPageToken() string

func (*ListTableSpecsResponse) GetTableSpecs 

func (m *ListTableSpecsResponse) GetTableSpecs() []*TableSpec

func (*ListTableSpecsResponse) ProtoMessage 

func (*ListTableSpecsResponse) ProtoMessage()

func (*ListTableSpecsResponse) Reset 

func (m *ListTableSpecsResponse) Reset()

func (*ListTableSpecsResponse) String 

func (m *ListTableSpecsResponse) String() string

func (*ListTableSpecsResponse) XXX_DiscardUnknown 

func (m *ListTableSpecsResponse) XXX_DiscardUnknown()

func (*ListTableSpecsResponse) XXX_Marshal 

func (m *ListTableSpecsResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ListTableSpecsResponse) XXX_Merge 

func (m *ListTableSpecsResponse) XXX_Merge(src proto.Message)

func (*ListTableSpecsResponse) XXX_Size 

func (m *ListTableSpecsResponse) XXX_Size() int

func (*ListTableSpecsResponse) XXX_Unmarshal 

func (m *ListTableSpecsResponse) XXX_Unmarshal(b []byte) error

type Model 

type Model struct {
	// Required.
	// The model metadata that is specific to the problem type.
	// Must match the metadata type of the dataset used to train the model.
	//
	// Types that are valid to be assigned to ModelMetadata:
	//	*Model_TranslationModelMetadata
	//	*Model_ImageClassificationModelMetadata
	//	*Model_TextClassificationModelMetadata
	//	*Model_ImageObjectDetectionModelMetadata
	//	*Model_VideoClassificationModelMetadata
	//	*Model_VideoObjectTrackingModelMetadata
	//	*Model_TextExtractionModelMetadata
	//	*Model_TablesModelMetadata
	//	*Model_TextSentimentModelMetadata
	ModelMetadata isModel_ModelMetadata `protobuf_oneof:"model_metadata"`
	// Output only. Resource name of the model.
	// Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The name of the model to show in the interface. The name can be
	// up to 32 characters long and can consist only of ASCII Latin letters A-Z
	// and a-z, underscores
	// (_), and ASCII digits 0-9. It must start with a letter.
	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Required. The resource ID of the dataset used to create the model. The dataset must
	// come from the same ancestor project and location.
	DatasetId string `protobuf:"bytes,3,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
	// Output only. Timestamp when the model training finished  and can be used for prediction.
	CreateTime *timestamp.Timestamp `protobuf:"bytes,7,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
	// Output only. Timestamp when this model was last updated.
	UpdateTime *timestamp.Timestamp `protobuf:"bytes,11,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
	// Output only. Deployment state of the model. A model can only serve
	// prediction requests after it gets deployed.
	DeploymentState      Model_DeploymentState `` /* 162-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}              `json:"-"`
	XXX_unrecognized     []byte                `json:"-"`
	XXX_sizecache        int32                 `json:"-"`
}

API proto representing a trained machine learning model.

func (*Model) Descriptor 

func (*Model) Descriptor() ([]byte, []int)

func (*Model) GetCreateTime 

func (m *Model) GetCreateTime() *timestamp.Timestamp

func (*Model) GetDatasetId 

func (m *Model) GetDatasetId() string

func (*Model) GetDeploymentState 

func (m *Model) GetDeploymentState() Model_DeploymentState

func (*Model) GetDisplayName 

func (m *Model) GetDisplayName() string

func (*Model) GetImageClassificationModelMetadata 

func (m *Model) GetImageClassificationModelMetadata() *ImageClassificationModelMetadata

func (*Model) GetImageObjectDetectionModelMetadata 

func (m *Model) GetImageObjectDetectionModelMetadata() *ImageObjectDetectionModelMetadata

func (*Model) GetModelMetadata 

func (m *Model) GetModelMetadata() isModel_ModelMetadata

func (*Model) GetName 

func (m *Model) GetName() string

func (*Model) GetTablesModelMetadata 

func (m *Model) GetTablesModelMetadata() *TablesModelMetadata

func (*Model) GetTextClassificationModelMetadata 

func (m *Model) GetTextClassificationModelMetadata() *TextClassificationModelMetadata

func (*Model) GetTextExtractionModelMetadata 

func (m *Model) GetTextExtractionModelMetadata() *TextExtractionModelMetadata

func (*Model) GetTextSentimentModelMetadata 

func (m *Model) GetTextSentimentModelMetadata() *TextSentimentModelMetadata

func (*Model) GetTranslationModelMetadata 

func (m *Model) GetTranslationModelMetadata() *TranslationModelMetadata

func (*Model) GetUpdateTime 

func (m *Model) GetUpdateTime() *timestamp.Timestamp

func (*Model) GetVideoClassificationModelMetadata 

func (m *Model) GetVideoClassificationModelMetadata() *VideoClassificationModelMetadata

func (*Model) GetVideoObjectTrackingModelMetadata 

func (m *Model) GetVideoObjectTrackingModelMetadata() *VideoObjectTrackingModelMetadata

func (*Model) ProtoMessage 

func (*Model) ProtoMessage()

func (*Model) Reset 

func (m *Model) Reset()

func (*Model) String 

func (m *Model) String() string

func (*Model) XXX_DiscardUnknown 

func (m *Model) XXX_DiscardUnknown()

func (*Model) XXX_Marshal 

func (m *Model) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*Model) XXX_Merge 

func (m *Model) XXX_Merge(src proto.Message)

func (*Model) XXX_OneofWrappers 

func (*Model) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*Model) XXX_Size 

func (m *Model) XXX_Size() int

func (*Model) XXX_Unmarshal 

func (m *Model) XXX_Unmarshal(b []byte) error

type ModelEvaluation 

type ModelEvaluation struct {
	// Output only. Problem type specific evaluation metrics.
	//
	// Types that are valid to be assigned to Metrics:
	//	*ModelEvaluation_ClassificationEvaluationMetrics
	//	*ModelEvaluation_RegressionEvaluationMetrics
	//	*ModelEvaluation_TranslationEvaluationMetrics
	//	*ModelEvaluation_ImageObjectDetectionEvaluationMetrics
	//	*ModelEvaluation_VideoObjectTrackingEvaluationMetrics
	//	*ModelEvaluation_TextSentimentEvaluationMetrics
	//	*ModelEvaluation_TextExtractionEvaluationMetrics
	Metrics isModelEvaluation_Metrics `protobuf_oneof:"metrics"`
	// Output only. Resource name of the model evaluation.
	// Format:
	//
	// `projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}`
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Output only. The ID of the annotation spec that the model evaluation applies to. The
	// The ID is empty for the overall model evaluation.
	// For Tables annotation specs in the dataset do not exist and this ID is
	// always not set, but for CLASSIFICATION
	//
	// [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
	// the
	// [display_name][google.cloud.automl.v1beta1.ModelEvaluation.display_name]
	// field is used.
	AnnotationSpecId string `protobuf:"bytes,2,opt,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// Output only. The value of
	// [display_name][google.cloud.automl.v1beta1.AnnotationSpec.display_name] at
	// the moment when the model was trained. Because this field returns a value
	// at model training time, for different models trained from the same dataset,
	// the values may differ, since display names could had been changed between
	// the two model's trainings.
	// For Tables CLASSIFICATION
	//
	// [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
	// distinct values of the target column at the moment of the model evaluation
	// are populated here.
	// The display_name is empty for the overall model evaluation.
	DisplayName string `protobuf:"bytes,15,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only. Timestamp when this model evaluation was created.
	CreateTime *timestamp.Timestamp `protobuf:"bytes,5,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
	// Output only. The number of examples used for model evaluation, i.e. for
	// which ground truth from time of model creation is compared against the
	// predicted annotations created by the model.
	// For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is
	// the total number of all examples used for evaluation.
	// Otherwise, this is the count of examples that according to the ground
	// truth were annotated by the
	//
	// [annotation_spec_id][google.cloud.automl.v1beta1.ModelEvaluation.annotation_spec_id].
	EvaluatedExampleCount int32    `` /* 127-byte string literal not displayed */
	XXX_NoUnkeyedLiteral  struct{} `json:"-"`
	XXX_unrecognized      []byte   `json:"-"`
	XXX_sizecache         int32    `json:"-"`
}

Evaluation results of a model.

func (*ModelEvaluation) Descriptor 

func (*ModelEvaluation) Descriptor() ([]byte, []int)

func (*ModelEvaluation) GetAnnotationSpecId 

func (m *ModelEvaluation) GetAnnotationSpecId() string

func (*ModelEvaluation) GetClassificationEvaluationMetrics 

func (m *ModelEvaluation) GetClassificationEvaluationMetrics() *ClassificationEvaluationMetrics

func (*ModelEvaluation) GetCreateTime 

func (m *ModelEvaluation) GetCreateTime() *timestamp.Timestamp

func (*ModelEvaluation) GetDisplayName 

func (m *ModelEvaluation) GetDisplayName() string

func (*ModelEvaluation) GetEvaluatedExampleCount 

func (m *ModelEvaluation) GetEvaluatedExampleCount() int32

func (*ModelEvaluation) GetImageObjectDetectionEvaluationMetrics 

func (m *ModelEvaluation) GetImageObjectDetectionEvaluationMetrics() *ImageObjectDetectionEvaluationMetrics

func (*ModelEvaluation) GetMetrics 

func (m *ModelEvaluation) GetMetrics() isModelEvaluation_Metrics

func (*ModelEvaluation) GetName 

func (m *ModelEvaluation) GetName() string

func (*ModelEvaluation) GetRegressionEvaluationMetrics 

func (m *ModelEvaluation) GetRegressionEvaluationMetrics() *RegressionEvaluationMetrics

func (*ModelEvaluation) GetTextExtractionEvaluationMetrics 

func (m *ModelEvaluation) GetTextExtractionEvaluationMetrics() *TextExtractionEvaluationMetrics

func (*ModelEvaluation) GetTextSentimentEvaluationMetrics 

func (m *ModelEvaluation) GetTextSentimentEvaluationMetrics() *TextSentimentEvaluationMetrics

func (*ModelEvaluation) GetTranslationEvaluationMetrics 

func (m *ModelEvaluation) GetTranslationEvaluationMetrics() *TranslationEvaluationMetrics

func (*ModelEvaluation) GetVideoObjectTrackingEvaluationMetrics 

func (m *ModelEvaluation) GetVideoObjectTrackingEvaluationMetrics() *VideoObjectTrackingEvaluationMetrics

func (*ModelEvaluation) ProtoMessage 

func (*ModelEvaluation) ProtoMessage()

func (*ModelEvaluation) Reset 

func (m *ModelEvaluation) Reset()

func (*ModelEvaluation) String 

func (m *ModelEvaluation) String() string

func (*ModelEvaluation) XXX_DiscardUnknown 

func (m *ModelEvaluation) XXX_DiscardUnknown()

func (*ModelEvaluation) XXX_Marshal 

func (m *ModelEvaluation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ModelEvaluation) XXX_Merge 

func (m *ModelEvaluation) XXX_Merge(src proto.Message)

func (*ModelEvaluation) XXX_OneofWrappers 

func (*ModelEvaluation) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*ModelEvaluation) XXX_Size 

func (m *ModelEvaluation) XXX_Size() int

func (*ModelEvaluation) XXX_Unmarshal 

func (m *ModelEvaluation) XXX_Unmarshal(b []byte) error

type ModelEvaluation_ClassificationEvaluationMetrics 

type ModelEvaluation_ClassificationEvaluationMetrics struct {
	ClassificationEvaluationMetrics *ClassificationEvaluationMetrics `protobuf:"bytes,8,opt,name=classification_evaluation_metrics,json=classificationEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_ImageObjectDetectionEvaluationMetrics 

type ModelEvaluation_ImageObjectDetectionEvaluationMetrics struct {
	ImageObjectDetectionEvaluationMetrics *ImageObjectDetectionEvaluationMetrics `` /* 126-byte string literal not displayed */
}

type ModelEvaluation_RegressionEvaluationMetrics 

type ModelEvaluation_RegressionEvaluationMetrics struct {
	RegressionEvaluationMetrics *RegressionEvaluationMetrics `protobuf:"bytes,24,opt,name=regression_evaluation_metrics,json=regressionEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TextExtractionEvaluationMetrics 

type ModelEvaluation_TextExtractionEvaluationMetrics struct {
	TextExtractionEvaluationMetrics *TextExtractionEvaluationMetrics `protobuf:"bytes,13,opt,name=text_extraction_evaluation_metrics,json=textExtractionEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TextSentimentEvaluationMetrics 

type ModelEvaluation_TextSentimentEvaluationMetrics struct {
	TextSentimentEvaluationMetrics *TextSentimentEvaluationMetrics `protobuf:"bytes,11,opt,name=text_sentiment_evaluation_metrics,json=textSentimentEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TranslationEvaluationMetrics 

type ModelEvaluation_TranslationEvaluationMetrics struct {
	TranslationEvaluationMetrics *TranslationEvaluationMetrics `protobuf:"bytes,9,opt,name=translation_evaluation_metrics,json=translationEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_VideoObjectTrackingEvaluationMetrics 

type ModelEvaluation_VideoObjectTrackingEvaluationMetrics struct {
	VideoObjectTrackingEvaluationMetrics *VideoObjectTrackingEvaluationMetrics `protobuf:"bytes,14,opt,name=video_object_tracking_evaluation_metrics,json=videoObjectTrackingEvaluationMetrics,proto3,oneof"`
}

type ModelExportOutputConfig 

type ModelExportOutputConfig struct {
	// Required. The destination of the output.
	//
	// Types that are valid to be assigned to Destination:
	//	*ModelExportOutputConfig_GcsDestination
	//	*ModelExportOutputConfig_GcrDestination
	Destination isModelExportOutputConfig_Destination `protobuf_oneof:"destination"`
	// The format in which the model must be exported. The available, and default,
	// formats depend on the problem and model type (if given problem and type
	// combination doesn't have a format listed, it means its models are not
	// exportable):
	//
	// *  For Image Classification mobile-low-latency-1, mobile-versatile-1,
	//        mobile-high-accuracy-1:
	//      "tflite" (default), "edgetpu_tflite", "tf_saved_model", "tf_js",
	//      "docker".
	//
	// *  For Image Classification mobile-core-ml-low-latency-1,
	//        mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1:
	//      "core_ml" (default).
	// Formats description:
	//
	// * tflite - Used for Android mobile devices.
	// * edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/)
	//                    devices.
	// * tf_saved_model - A tensorflow model in SavedModel format.
	// * tf_js - A [TensorFlow.js](https://www.tensorflow.org/js) model that can
	//           be used in the browser and in Node.js using JavaScript.
	// * docker - Used for Docker containers. Use the params field to customize
	//            the container. The container is verified to work correctly on
	//            ubuntu 16.04 operating system. See more at
	//            [containers
	//
	// quickstart](https:
	// //cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
	// * core_ml - Used for iOS mobile devices.
	ModelFormat string `protobuf:"bytes,4,opt,name=model_format,json=modelFormat,proto3" json:"model_format,omitempty"`
	// Additional model-type and format specific parameters describing the
	// requirements for the to be exported model files, any string must be up to
	// 25000 characters long.
	//
	//  * For `docker` format:
	//     `cpu_architecture` - (string) "x86_64" (default).
	//     `gpu_architecture` - (string) "none" (default), "nvidia".
	Params               map[string]string `` /* 153-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}          `json:"-"`
	XXX_unrecognized     []byte            `json:"-"`
	XXX_sizecache        int32             `json:"-"`
}

Output configuration for ModelExport Action.

func (*ModelExportOutputConfig) Descriptor 

func (*ModelExportOutputConfig) Descriptor() ([]byte, []int)

func (*ModelExportOutputConfig) GetDestination 

func (m *ModelExportOutputConfig) GetDestination() isModelExportOutputConfig_Destination

func (*ModelExportOutputConfig) GetGcrDestination 

func (m *ModelExportOutputConfig) GetGcrDestination() *GcrDestination

func (*ModelExportOutputConfig) GetGcsDestination 

func (m *ModelExportOutputConfig) GetGcsDestination() *GcsDestination

func (*ModelExportOutputConfig) GetModelFormat 

func (m *ModelExportOutputConfig) GetModelFormat() string

func (*ModelExportOutputConfig) GetParams 

func (m *ModelExportOutputConfig) GetParams() map[string]string

func (*ModelExportOutputConfig) ProtoMessage 

func (*ModelExportOutputConfig) ProtoMessage()

func (*ModelExportOutputConfig) Reset 

func (m *ModelExportOutputConfig) Reset()

func (*ModelExportOutputConfig) String 

func (m *ModelExportOutputConfig) String() string

func (*ModelExportOutputConfig) XXX_DiscardUnknown 

func (m *ModelExportOutputConfig) XXX_DiscardUnknown()

func (*ModelExportOutputConfig) XXX_Marshal 

func (m *ModelExportOutputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*ModelExportOutputConfig) XXX_Merge 

func (m *ModelExportOutputConfig) XXX_Merge(src proto.Message)

func (*ModelExportOutputConfig) XXX_OneofWrappers 

func (*ModelExportOutputConfig) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*ModelExportOutputConfig) XXX_Size 

func (m *ModelExportOutputConfig) XXX_Size() int

func (*ModelExportOutputConfig) XXX_Unmarshal 

func (m *ModelExportOutputConfig) XXX_Unmarshal(b []byte) error

type ModelExportOutputConfig_GcrDestination 

type ModelExportOutputConfig_GcrDestination struct {
	GcrDestination *GcrDestination `protobuf:"bytes,3,opt,name=gcr_destination,json=gcrDestination,proto3,oneof"`
}

type ModelExportOutputConfig_GcsDestination 

type ModelExportOutputConfig_GcsDestination struct {
	GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

type Model_DeploymentState 

type Model_DeploymentState int32

Deployment state of the model. 

const (
	// Should not be used, an un-set enum has this value by default.
	Model_DEPLOYMENT_STATE_UNSPECIFIED Model_DeploymentState = 0
	// Model is deployed.
	Model_DEPLOYED Model_DeploymentState = 1
	// Model is not deployed.
	Model_UNDEPLOYED Model_DeploymentState = 2
)

func (Model_DeploymentState) EnumDescriptor 

func (Model_DeploymentState) EnumDescriptor() ([]byte, []int)

func (Model_DeploymentState) String 

func (x Model_DeploymentState) String() string

type Model_ImageClassificationModelMetadata 

type Model_ImageClassificationModelMetadata struct {
	ImageClassificationModelMetadata *ImageClassificationModelMetadata `protobuf:"bytes,13,opt,name=image_classification_model_metadata,json=imageClassificationModelMetadata,proto3,oneof"`
}

type Model_ImageObjectDetectionModelMetadata 

type Model_ImageObjectDetectionModelMetadata struct {
	ImageObjectDetectionModelMetadata *ImageObjectDetectionModelMetadata `protobuf:"bytes,20,opt,name=image_object_detection_model_metadata,json=imageObjectDetectionModelMetadata,proto3,oneof"`
}

type Model_TablesModelMetadata 

type Model_TablesModelMetadata struct {
	TablesModelMetadata *TablesModelMetadata `protobuf:"bytes,24,opt,name=tables_model_metadata,json=tablesModelMetadata,proto3,oneof"`
}

type Model_TextClassificationModelMetadata 

type Model_TextClassificationModelMetadata struct {
	TextClassificationModelMetadata *TextClassificationModelMetadata `protobuf:"bytes,14,opt,name=text_classification_model_metadata,json=textClassificationModelMetadata,proto3,oneof"`
}

type Model_TextExtractionModelMetadata 

type Model_TextExtractionModelMetadata struct {
	TextExtractionModelMetadata *TextExtractionModelMetadata `protobuf:"bytes,19,opt,name=text_extraction_model_metadata,json=textExtractionModelMetadata,proto3,oneof"`
}

type Model_TextSentimentModelMetadata 

type Model_TextSentimentModelMetadata struct {
	TextSentimentModelMetadata *TextSentimentModelMetadata `protobuf:"bytes,22,opt,name=text_sentiment_model_metadata,json=textSentimentModelMetadata,proto3,oneof"`
}

type Model_TranslationModelMetadata 

type Model_TranslationModelMetadata struct {
	TranslationModelMetadata *TranslationModelMetadata `protobuf:"bytes,15,opt,name=translation_model_metadata,json=translationModelMetadata,proto3,oneof"`
}

type Model_VideoClassificationModelMetadata 

type Model_VideoClassificationModelMetadata struct {
	VideoClassificationModelMetadata *VideoClassificationModelMetadata `protobuf:"bytes,23,opt,name=video_classification_model_metadata,json=videoClassificationModelMetadata,proto3,oneof"`
}

type Model_VideoObjectTrackingModelMetadata 

type Model_VideoObjectTrackingModelMetadata struct {
	VideoObjectTrackingModelMetadata *VideoObjectTrackingModelMetadata `protobuf:"bytes,21,opt,name=video_object_tracking_model_metadata,json=videoObjectTrackingModelMetadata,proto3,oneof"`
}

type NormalizedVertex 

type NormalizedVertex struct {
	// Required. Horizontal coordinate.
	X float32 `protobuf:"fixed32,1,opt,name=x,proto3" json:"x,omitempty"`
	// Required. Vertical coordinate.
	Y                    float32  `protobuf:"fixed32,2,opt,name=y,proto3" json:"y,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.

func (*NormalizedVertex) Descriptor 

func (*NormalizedVertex) Descriptor() ([]byte, []int)

func (*NormalizedVertex) GetX 

func (m *NormalizedVertex) GetX() float32

func (*NormalizedVertex) GetY 

func (m *NormalizedVertex) GetY() float32

func (*NormalizedVertex) ProtoMessage 

func (*NormalizedVertex) ProtoMessage()

func (*NormalizedVertex) Reset 

func (m *NormalizedVertex) Reset()

func (*NormalizedVertex) String 

func (m *NormalizedVertex) String() string

func (*NormalizedVertex) XXX_DiscardUnknown 

func (m *NormalizedVertex) XXX_DiscardUnknown()

func (*NormalizedVertex) XXX_Marshal 

func (m *NormalizedVertex) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*NormalizedVertex) XXX_Merge 

func (m *NormalizedVertex) XXX_Merge(src proto.Message)

func (*NormalizedVertex) XXX_Size 

func (m *NormalizedVertex) XXX_Size() int

func (*NormalizedVertex) XXX_Unmarshal 

func (m *NormalizedVertex) XXX_Unmarshal(b []byte) error

type OperationMetadata 

type OperationMetadata struct {
	// Ouptut only. Details of specific operation. Even if this field is empty,
	// the presence allows to distinguish different types of operations.
	//
	// Types that are valid to be assigned to Details:
	//	*OperationMetadata_DeleteDetails
	//	*OperationMetadata_DeployModelDetails
	//	*OperationMetadata_UndeployModelDetails
	//	*OperationMetadata_CreateModelDetails
	//	*OperationMetadata_ImportDataDetails
	//	*OperationMetadata_BatchPredictDetails
	//	*OperationMetadata_ExportDataDetails
	//	*OperationMetadata_ExportModelDetails
	//	*OperationMetadata_ExportEvaluatedExamplesDetails
	Details isOperationMetadata_Details `protobuf_oneof:"details"`
	// Output only. Progress of operation. Range: [0, 100].
	// Not used currently.
	ProgressPercent int32 `protobuf:"varint,13,opt,name=progress_percent,json=progressPercent,proto3" json:"progress_percent,omitempty"`
	// Output only. Partial failures encountered.
	// E.g. single files that couldn't be read.
	// This field should never exceed 20 entries.
	// Status details field will contain standard GCP error details.
	PartialFailures []*status.Status `protobuf:"bytes,2,rep,name=partial_failures,json=partialFailures,proto3" json:"partial_failures,omitempty"`
	// Output only. Time when the operation was created.
	CreateTime *timestamp.Timestamp `protobuf:"bytes,3,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
	// Output only. Time when the operation was updated for the last time.
	UpdateTime           *timestamp.Timestamp `protobuf:"bytes,4,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
	XXX_NoUnkeyedLiteral struct{}             `json:"-"`
	XXX_unrecognized     []byte               `json:"-"`
	XXX_sizecache        int32                `json:"-"`
}

Metadata used across all long running operations returned by AutoML API.

func (*OperationMetadata) Descriptor 

func (*OperationMetadata) Descriptor() ([]byte, []int)

func (*OperationMetadata) GetBatchPredictDetails 

func (m *OperationMetadata) GetBatchPredictDetails() *BatchPredictOperationMetadata

func (*OperationMetadata) GetCreateModelDetails 

func (m *OperationMetadata) GetCreateModelDetails() *CreateModelOperationMetadata

func (*OperationMetadata) GetCreateTime 

func (m *OperationMetadata) GetCreateTime() *timestamp.Timestamp

func (*OperationMetadata) GetDeleteDetails 

func (m *OperationMetadata) GetDeleteDetails() *DeleteOperationMetadata

func (*OperationMetadata) GetDeployModelDetails 

func (m *OperationMetadata) GetDeployModelDetails() *DeployModelOperationMetadata

func (*OperationMetadata) GetDetails 

func (m *OperationMetadata) GetDetails() isOperationMetadata_Details

func (*OperationMetadata) GetExportDataDetails 

func (m *OperationMetadata) GetExportDataDetails() *ExportDataOperationMetadata

func (*OperationMetadata) GetExportEvaluatedExamplesDetails 

func (m *OperationMetadata) GetExportEvaluatedExamplesDetails() *ExportEvaluatedExamplesOperationMetadata

func (*OperationMetadata) GetExportModelDetails 

func (m *OperationMetadata) GetExportModelDetails() *ExportModelOperationMetadata

func (*OperationMetadata) GetImportDataDetails 

func (m *OperationMetadata) GetImportDataDetails() *ImportDataOperationMetadata

func (*OperationMetadata) GetPartialFailures 

func (m *OperationMetadata) GetPartialFailures() []*status.Status

func (*OperationMetadata) GetProgressPercent 

func (m *OperationMetadata) GetProgressPercent() int32

func (*OperationMetadata) GetUndeployModelDetails 

func (m *OperationMetadata) GetUndeployModelDetails() *UndeployModelOperationMetadata

func (*OperationMetadata) GetUpdateTime 

func (m *OperationMetadata) GetUpdateTime() *timestamp.Timestamp

func (*OperationMetadata) ProtoMessage 

func (*OperationMetadata) ProtoMessage()

func (*OperationMetadata) Reset 

func (m *OperationMetadata) Reset()

func (*OperationMetadata) String 

func (m *OperationMetadata) String() string

func (*OperationMetadata) XXX_DiscardUnknown 

func (m *OperationMetadata) XXX_DiscardUnknown()

func (*OperationMetadata) XXX_Marshal 

func (m *OperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*OperationMetadata) XXX_Merge 

func (m *OperationMetadata) XXX_Merge(src proto.Message)

func (*OperationMetadata) XXX_OneofWrappers 

func (*OperationMetadata) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*OperationMetadata) XXX_Size 

func (m *OperationMetadata) XXX_Size() int

func (*OperationMetadata) XXX_Unmarshal 

func (m *OperationMetadata) XXX_Unmarshal(b []byte) error

type OperationMetadata_BatchPredictDetails 

type OperationMetadata_BatchPredictDetails struct {
	BatchPredictDetails *BatchPredictOperationMetadata `protobuf:"bytes,16,opt,name=batch_predict_details,json=batchPredictDetails,proto3,oneof"`
}

type OperationMetadata_CreateModelDetails 

type OperationMetadata_CreateModelDetails struct {
	CreateModelDetails *CreateModelOperationMetadata `protobuf:"bytes,10,opt,name=create_model_details,json=createModelDetails,proto3,oneof"`
}

type OperationMetadata_DeleteDetails 

type OperationMetadata_DeleteDetails struct {
	DeleteDetails *DeleteOperationMetadata `protobuf:"bytes,8,opt,name=delete_details,json=deleteDetails,proto3,oneof"`
}

type OperationMetadata_DeployModelDetails 

type OperationMetadata_DeployModelDetails struct {
	DeployModelDetails *DeployModelOperationMetadata `protobuf:"bytes,24,opt,name=deploy_model_details,json=deployModelDetails,proto3,oneof"`
}

type OperationMetadata_ExportDataDetails 

type OperationMetadata_ExportDataDetails struct {
	ExportDataDetails *ExportDataOperationMetadata `protobuf:"bytes,21,opt,name=export_data_details,json=exportDataDetails,proto3,oneof"`
}

type OperationMetadata_ExportEvaluatedExamplesDetails 

type OperationMetadata_ExportEvaluatedExamplesDetails struct {
	ExportEvaluatedExamplesDetails *ExportEvaluatedExamplesOperationMetadata `protobuf:"bytes,26,opt,name=export_evaluated_examples_details,json=exportEvaluatedExamplesDetails,proto3,oneof"`
}

type OperationMetadata_ExportModelDetails 

type OperationMetadata_ExportModelDetails struct {
	ExportModelDetails *ExportModelOperationMetadata `protobuf:"bytes,22,opt,name=export_model_details,json=exportModelDetails,proto3,oneof"`
}

type OperationMetadata_ImportDataDetails 

type OperationMetadata_ImportDataDetails struct {
	ImportDataDetails *ImportDataOperationMetadata `protobuf:"bytes,15,opt,name=import_data_details,json=importDataDetails,proto3,oneof"`
}

type OperationMetadata_UndeployModelDetails 

type OperationMetadata_UndeployModelDetails struct {
	UndeployModelDetails *UndeployModelOperationMetadata `protobuf:"bytes,25,opt,name=undeploy_model_details,json=undeployModelDetails,proto3,oneof"`
}

type OutputConfig 

type OutputConfig struct {
	// Required. The destination of the output.
	//
	// Types that are valid to be assigned to Destination:
	//	*OutputConfig_GcsDestination
	//	*OutputConfig_BigqueryDestination
	Destination          isOutputConfig_Destination `protobuf_oneof:"destination"`
	XXX_NoUnkeyedLiteral struct{}                   `json:"-"`
	XXX_unrecognized     []byte                     `json:"-"`
	XXX_sizecache        int32                      `json:"-"`
}

  • For Translation: CSV file `translation.csv`, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language)

  • For Tables: Output depends on whether the dataset was imported from GCS or BigQuery. GCS case:

[gcs_destination][google.cloud.automl.v1beta1.OutputConfig.gcs_destination] 

  must be set. Exported are CSV file(s) `tables_1.csv`,
  `tables_2.csv`,...,`tables_N.csv` with each having as header line
  the table's column names, and all other lines contain values for
  the header columns.
BigQuery case:

[bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination] 

pointing to a BigQuery project must be set. In the given project a
new dataset will be created with name

`export_data_<automl-dataset-display-name>_<timestamp-of-export-call>` 

where <automl-dataset-display-name> will be made
BigQuery-dataset-name compatible (e.g. most special characters will
become underscores), and timestamp will be in
YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that
dataset a new table called `primary_table` will be created, and
filled with precisely the same data as this obtained on import.

func (*OutputConfig) Descriptor 

func (*OutputConfig) Descriptor() ([]byte, []int)

func (*OutputConfig) GetBigqueryDestination 

func (m *OutputConfig) GetBigqueryDestination() *BigQueryDestination

func (*OutputConfig) GetDestination 

func (m *OutputConfig) GetDestination() isOutputConfig_Destination

func (*OutputConfig) GetGcsDestination 

func (m *OutputConfig) GetGcsDestination() *GcsDestination

func (*OutputConfig) ProtoMessage 

func (*OutputConfig) ProtoMessage()

func (*OutputConfig) Reset 

func (m *OutputConfig) Reset()

func (*OutputConfig) String 

func (m *OutputConfig) String() string

func (*OutputConfig) XXX_DiscardUnknown 

func (m *OutputConfig) XXX_DiscardUnknown()

func (*OutputConfig) XXX_Marshal 

func (m *OutputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*OutputConfig) XXX_Merge 

func (m *OutputConfig) XXX_Merge(src proto.Message)

func (*OutputConfig) XXX_OneofWrappers 

func (*OutputConfig) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*OutputConfig) XXX_Size 

func (m *OutputConfig) XXX_Size() int

func (*OutputConfig) XXX_Unmarshal 

func (m *OutputConfig) XXX_Unmarshal(b []byte) error

type OutputConfig_BigqueryDestination 

type OutputConfig_BigqueryDestination struct {
	BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}

type OutputConfig_GcsDestination 

type OutputConfig_GcsDestination struct {
	GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

type PredictRequest 

type PredictRequest struct {
	// Required. Name of the model requested to serve the prediction.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. Payload to perform a prediction on. The payload must match the
	// problem type that the model was trained to solve.
	Payload *ExamplePayload `protobuf:"bytes,2,opt,name=payload,proto3" json:"payload,omitempty"`
	// Additional domain-specific parameters, any string must be up to 25000
	// characters long.
	//
	// *  For Image Classification:
	//
	//    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
	//     makes predictions for an image, it will only produce results that have
	//     at least this confidence score. The default is 0.5.
	//
	//  *  For Image Object Detection:
	//    `score_threshold` - (float) When Model detects objects on the image,
	//        it will only produce bounding boxes which have at least this
	//        confidence score. Value in 0 to 1 range, default is 0.5.
	//    `max_bounding_box_count` - (int64) No more than this number of bounding
	//        boxes will be returned in the response. Default is 100, the
	//        requested value may be limited by server.
	// *  For Tables:
	//    feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
	//        should be populated in the returned TablesAnnotation.
	//        The default is false.
	Params               map[string]string `` /* 153-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}          `json:"-"`
	XXX_unrecognized     []byte            `json:"-"`
	XXX_sizecache        int32             `json:"-"`
}

Request message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].

func (*PredictRequest) Descriptor 

func (*PredictRequest) Descriptor() ([]byte, []int)

func (*PredictRequest) GetName 

func (m *PredictRequest) GetName() string

func (*PredictRequest) GetParams 

func (m *PredictRequest) GetParams() map[string]string

func (*PredictRequest) GetPayload 

func (m *PredictRequest) GetPayload() *ExamplePayload

func (*PredictRequest) ProtoMessage 

func (*PredictRequest) ProtoMessage()

func (*PredictRequest) Reset 

func (m *PredictRequest) Reset()

func (*PredictRequest) String 

func (m *PredictRequest) String() string

func (*PredictRequest) XXX_DiscardUnknown 

func (m *PredictRequest) XXX_DiscardUnknown()

func (*PredictRequest) XXX_Marshal 

func (m *PredictRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*PredictRequest) XXX_Merge 

func (m *PredictRequest) XXX_Merge(src proto.Message)

func (*PredictRequest) XXX_Size 

func (m *PredictRequest) XXX_Size() int

func (*PredictRequest) XXX_Unmarshal 

func (m *PredictRequest) XXX_Unmarshal(b []byte) error

type PredictResponse 

type PredictResponse struct {
	// Prediction result.
	// Translation and Text Sentiment will return precisely one payload.
	Payload []*AnnotationPayload `protobuf:"bytes,1,rep,name=payload,proto3" json:"payload,omitempty"`
	// The preprocessed example that AutoML actually makes prediction on.
	// Empty if AutoML does not preprocess the input example.
	// * For Text Extraction:
	//   If the input is a .pdf file, the OCR'ed text will be provided in
	//   [document_text][google.cloud.automl.v1beta1.Document.document_text].
	PreprocessedInput *ExamplePayload `protobuf:"bytes,3,opt,name=preprocessed_input,json=preprocessedInput,proto3" json:"preprocessed_input,omitempty"`
	// Additional domain-specific prediction response metadata.
	//
	// * For Image Object Detection:
	//  `max_bounding_box_count` - (int64) At most that many bounding boxes per
	//      image could have been returned.
	//
	// * For Text Sentiment:
	//  `sentiment_score` - (float, deprecated) A value between -1 and 1,
	//      -1 maps to least positive sentiment, while 1 maps to the most positive
	//      one and the higher the score, the more positive the sentiment in the
	//      document is. Yet these values are relative to the training data, so
	//      e.g. if all data was positive then -1 will be also positive (though
	//      the least).
	//      The sentiment_score shouldn't be confused with "score" or "magnitude"
	//      from the previous Natural Language Sentiment Analysis API.
	Metadata             map[string]string `` /* 157-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}          `json:"-"`
	XXX_unrecognized     []byte            `json:"-"`
	XXX_sizecache        int32             `json:"-"`
}

Response message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].

func (*PredictResponse) Descriptor 

func (*PredictResponse) Descriptor() ([]byte, []int)

func (*PredictResponse) GetMetadata 

func (m *PredictResponse) GetMetadata() map[string]string

func (*PredictResponse) GetPayload 

func (m *PredictResponse) GetPayload() []*AnnotationPayload

func (*PredictResponse) GetPreprocessedInput 

func (m *PredictResponse) GetPreprocessedInput() *ExamplePayload

func (*PredictResponse) ProtoMessage 

func (*PredictResponse) ProtoMessage()

func (*PredictResponse) Reset 

func (m *PredictResponse) Reset()

func (*PredictResponse) String 

func (m *PredictResponse) String() string

func (*PredictResponse) XXX_DiscardUnknown 

func (m *PredictResponse) XXX_DiscardUnknown()

func (*PredictResponse) XXX_Marshal 

func (m *PredictResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*PredictResponse) XXX_Merge 

func (m *PredictResponse) XXX_Merge(src proto.Message)

func (*PredictResponse) XXX_Size 

func (m *PredictResponse) XXX_Size() int

func (*PredictResponse) XXX_Unmarshal 

func (m *PredictResponse) XXX_Unmarshal(b []byte) error

type PredictionServiceClient 

type PredictionServiceClient interface {
	// Perform an online prediction. The prediction result will be directly
	// returned in the response.
	// Available for following ML problems, and their expected request payloads:
	// * Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes
	//                          up to 30MB.
	// * Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes
	//                            up to 30MB.
	// * Text Classification - TextSnippet, content up to 60,000 characters,
	//                         UTF-8 encoded.
	// * Text Extraction - TextSnippet, content up to 30,000 characters,
	//                     UTF-8 NFC encoded.
	// * Translation - TextSnippet, content up to 25,000 characters, UTF-8
	//                 encoded.
	// * Tables - Row, with column values matching the columns of the model,
	//            up to 5MB. Not available for FORECASTING
	//
	// [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type].
	// * Text Sentiment - TextSnippet, content up 500 characters, UTF-8
	//                     encoded.
	Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*PredictResponse, error)
	// Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1beta1.PredictionService.Predict], batch
	// prediction result won't be immediately available in the response. Instead,
	// a long running operation object is returned. User can poll the operation
	// result via [GetOperation][google.longrunning.Operations.GetOperation]
	// method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1beta1.BatchPredictResult] is returned in
	// the [response][google.longrunning.Operation.response] field.
	// Available for following ML problems:
	// * Image Classification
	// * Image Object Detection
	// * Video Classification
	// * Video Object Tracking * Text Extraction
	// * Tables
	BatchPredict(ctx context.Context, in *BatchPredictRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
}

PredictionServiceClient is the client API for PredictionService service.

For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.

func NewPredictionServiceClient 

func NewPredictionServiceClient(cc grpc.ClientConnInterface) PredictionServiceClient

type PredictionServiceServer 

type PredictionServiceServer interface {
	// Perform an online prediction. The prediction result will be directly
	// returned in the response.
	// Available for following ML problems, and their expected request payloads:
	// * Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes
	//                          up to 30MB.
	// * Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes
	//                            up to 30MB.
	// * Text Classification - TextSnippet, content up to 60,000 characters,
	//                         UTF-8 encoded.
	// * Text Extraction - TextSnippet, content up to 30,000 characters,
	//                     UTF-8 NFC encoded.
	// * Translation - TextSnippet, content up to 25,000 characters, UTF-8
	//                 encoded.
	// * Tables - Row, with column values matching the columns of the model,
	//            up to 5MB. Not available for FORECASTING
	//
	// [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type].
	// * Text Sentiment - TextSnippet, content up 500 characters, UTF-8
	//                     encoded.
	Predict(context.Context, *PredictRequest) (*PredictResponse, error)
	// Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1beta1.PredictionService.Predict], batch
	// prediction result won't be immediately available in the response. Instead,
	// a long running operation object is returned. User can poll the operation
	// result via [GetOperation][google.longrunning.Operations.GetOperation]
	// method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1beta1.BatchPredictResult] is returned in
	// the [response][google.longrunning.Operation.response] field.
	// Available for following ML problems:
	// * Image Classification
	// * Image Object Detection
	// * Video Classification
	// * Video Object Tracking * Text Extraction
	// * Tables
	BatchPredict(context.Context, *BatchPredictRequest) (*longrunning.Operation, error)
}

PredictionServiceServer is the server API for PredictionService service.

type RegressionEvaluationMetrics 

type RegressionEvaluationMetrics struct {
	// Output only. Root Mean Squared Error (RMSE).
	RootMeanSquaredError float32 `` /* 127-byte string literal not displayed */
	// Output only. Mean Absolute Error (MAE).
	MeanAbsoluteError float32 `protobuf:"fixed32,2,opt,name=mean_absolute_error,json=meanAbsoluteError,proto3" json:"mean_absolute_error,omitempty"`
	// Output only. Mean absolute percentage error. Only set if all ground truth
	// values are are positive.
	MeanAbsolutePercentageError float32 `` /* 148-byte string literal not displayed */
	// Output only. R squared.
	RSquared float32 `protobuf:"fixed32,4,opt,name=r_squared,json=rSquared,proto3" json:"r_squared,omitempty"`
	// Output only. Root mean squared log error.
	RootMeanSquaredLogError float32  `` /* 138-byte string literal not displayed */
	XXX_NoUnkeyedLiteral    struct{} `json:"-"`
	XXX_unrecognized        []byte   `json:"-"`
	XXX_sizecache           int32    `json:"-"`
}

Metrics for regression problems.

func (*RegressionEvaluationMetrics) Descriptor 

func (*RegressionEvaluationMetrics) Descriptor() ([]byte, []int)

func (*RegressionEvaluationMetrics) GetMeanAbsoluteError 

func (m *RegressionEvaluationMetrics) GetMeanAbsoluteError() float32

func (*RegressionEvaluationMetrics) GetMeanAbsolutePercentageError 

func (m *RegressionEvaluationMetrics) GetMeanAbsolutePercentageError() float32

func (*RegressionEvaluationMetrics) GetRSquared 

func (m *RegressionEvaluationMetrics) GetRSquared() float32

func (*RegressionEvaluationMetrics) GetRootMeanSquaredError 

func (m *RegressionEvaluationMetrics) GetRootMeanSquaredError() float32

func (*RegressionEvaluationMetrics) GetRootMeanSquaredLogError 

func (m *RegressionEvaluationMetrics) GetRootMeanSquaredLogError() float32

func (*RegressionEvaluationMetrics) ProtoMessage 

func (*RegressionEvaluationMetrics) ProtoMessage()

func (*RegressionEvaluationMetrics) Reset 

func (m *RegressionEvaluationMetrics) Reset()

func (*RegressionEvaluationMetrics) String 

func (m *RegressionEvaluationMetrics) String() string

func (*RegressionEvaluationMetrics) XXX_DiscardUnknown 

func (m *RegressionEvaluationMetrics) XXX_DiscardUnknown()

func (*RegressionEvaluationMetrics) XXX_Marshal 

func (m *RegressionEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*RegressionEvaluationMetrics) XXX_Merge 

func (m *RegressionEvaluationMetrics) XXX_Merge(src proto.Message)

func (*RegressionEvaluationMetrics) XXX_Size 

func (m *RegressionEvaluationMetrics) XXX_Size() int

func (*RegressionEvaluationMetrics) XXX_Unmarshal 

func (m *RegressionEvaluationMetrics) XXX_Unmarshal(b []byte) error

type Row 

type Row struct {
	// The resource IDs of the column specs describing the columns of the row.
	// If set must contain, but possibly in a different order, all input
	// feature
	//
	// [column_spec_ids][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
	// of the Model this row is being passed to.
	// Note: The below `values` field must match order of this field, if this
	// field is set.
	ColumnSpecIds []string `protobuf:"bytes,2,rep,name=column_spec_ids,json=columnSpecIds,proto3" json:"column_spec_ids,omitempty"`
	// Required. The values of the row cells, given in the same order as the
	// column_spec_ids, or, if not set, then in the same order as input
	// feature
	//
	// [column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
	// of the Model this row is being passed to.
	Values               []*_struct.Value `protobuf:"bytes,3,rep,name=values,proto3" json:"values,omitempty"`
	XXX_NoUnkeyedLiteral struct{}         `json:"-"`
	XXX_unrecognized     []byte           `json:"-"`
	XXX_sizecache        int32            `json:"-"`
}

A representation of a row in a relational table.

func (*Row) Descriptor 

func (*Row) Descriptor() ([]byte, []int)

func (*Row) GetColumnSpecIds 

func (m *Row) GetColumnSpecIds() []string

func (*Row) GetValues 

func (m *Row) GetValues() []*_struct.Value

func (*Row) ProtoMessage 

func (*Row) ProtoMessage()

func (*Row) Reset 

func (m *Row) Reset()

func (*Row) String 

func (m *Row) String() string

func (*Row) XXX_DiscardUnknown 

func (m *Row) XXX_DiscardUnknown()

func (*Row) XXX_Marshal 

func (m *Row) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*Row) XXX_Merge 

func (m *Row) XXX_Merge(src proto.Message)

func (*Row) XXX_Size 

func (m *Row) XXX_Size() int

func (*Row) XXX_Unmarshal 

func (m *Row) XXX_Unmarshal(b []byte) error

type StringStats 

type StringStats struct {
	// The statistics of the top 20 unigrams, ordered by
	// [count][google.cloud.automl.v1beta1.StringStats.UnigramStats.count].
	TopUnigramStats      []*StringStats_UnigramStats `protobuf:"bytes,1,rep,name=top_unigram_stats,json=topUnigramStats,proto3" json:"top_unigram_stats,omitempty"`
	XXX_NoUnkeyedLiteral struct{}                    `json:"-"`
	XXX_unrecognized     []byte                      `json:"-"`
	XXX_sizecache        int32                       `json:"-"`
}

The data statistics of a series of STRING values.

func (*StringStats) Descriptor 

func (*StringStats) Descriptor() ([]byte, []int)

func (*StringStats) GetTopUnigramStats 

func (m *StringStats) GetTopUnigramStats() []*StringStats_UnigramStats

func (*StringStats) ProtoMessage 

func (*StringStats) ProtoMessage()

func (*StringStats) Reset 

func (m *StringStats) Reset()

func (*StringStats) String 

func (m *StringStats) String() string

func (*StringStats) XXX_DiscardUnknown 

func (m *StringStats) XXX_DiscardUnknown()

func (*StringStats) XXX_Marshal 

func (m *StringStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*StringStats) XXX_Merge 

func (m *StringStats) XXX_Merge(src proto.Message)

func (*StringStats) XXX_Size 

func (m *StringStats) XXX_Size() int

func (*StringStats) XXX_Unmarshal 

func (m *StringStats) XXX_Unmarshal(b []byte) error

type StringStats_UnigramStats 

type StringStats_UnigramStats struct {
	// The unigram.
	Value string `protobuf:"bytes,1,opt,name=value,proto3" json:"value,omitempty"`
	// The number of occurrences of this unigram in the series.
	Count                int64    `protobuf:"varint,2,opt,name=count,proto3" json:"count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

The statistics of a unigram.

func (*StringStats_UnigramStats) Descriptor 

func (*StringStats_UnigramStats) Descriptor() ([]byte, []int)

func (*StringStats_UnigramStats) GetCount 

func (m *StringStats_UnigramStats) GetCount() int64

func (*StringStats_UnigramStats) GetValue 

func (m *StringStats_UnigramStats) GetValue() string

func (*StringStats_UnigramStats) ProtoMessage 

func (*StringStats_UnigramStats) ProtoMessage()

func (*StringStats_UnigramStats) Reset 

func (m *StringStats_UnigramStats) Reset()

func (*StringStats_UnigramStats) String 

func (m *StringStats_UnigramStats) String() string

func (*StringStats_UnigramStats) XXX_DiscardUnknown 

func (m *StringStats_UnigramStats) XXX_DiscardUnknown()

func (*StringStats_UnigramStats) XXX_Marshal 

func (m *StringStats_UnigramStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*StringStats_UnigramStats) XXX_Merge 

func (m *StringStats_UnigramStats) XXX_Merge(src proto.Message)

func (*StringStats_UnigramStats) XXX_Size 

func (m *StringStats_UnigramStats) XXX_Size() int

func (*StringStats_UnigramStats) XXX_Unmarshal 

func (m *StringStats_UnigramStats) XXX_Unmarshal(b []byte) error

type StructStats 

type StructStats struct {
	// Map from a field name of the struct to data stats aggregated over series
	// of all data in that field across all the structs.
	FieldStats           map[string]*DataStats `` /* 179-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}              `json:"-"`
	XXX_unrecognized     []byte                `json:"-"`
	XXX_sizecache        int32                 `json:"-"`
}

The data statistics of a series of STRUCT values.

func (*StructStats) Descriptor 

func (*StructStats) Descriptor() ([]byte, []int)

func (*StructStats) GetFieldStats 

func (m *StructStats) GetFieldStats() map[string]*DataStats

func (*StructStats) ProtoMessage 

func (*StructStats) ProtoMessage()

func (*StructStats) Reset 

func (m *StructStats) Reset()

func (*StructStats) String 

func (m *StructStats) String() string

func (*StructStats) XXX_DiscardUnknown 

func (m *StructStats) XXX_DiscardUnknown()

func (*StructStats) XXX_Marshal 

func (m *StructStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*StructStats) XXX_Merge 

func (m *StructStats) XXX_Merge(src proto.Message)

func (*StructStats) XXX_Size 

func (m *StructStats) XXX_Size() int

func (*StructStats) XXX_Unmarshal 

func (m *StructStats) XXX_Unmarshal(b []byte) error

type StructType 

type StructType struct {
	// Unordered map of struct field names to their data types.
	// Fields cannot be added or removed via Update. Their names and
	// data types are still mutable.
	Fields               map[string]*DataType `` /* 153-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}             `json:"-"`
	XXX_unrecognized     []byte               `json:"-"`
	XXX_sizecache        int32                `json:"-"`
}

`StructType` defines the DataType-s of a [STRUCT][google.cloud.automl.v1beta1.TypeCode.STRUCT] type.

func (*StructType) Descriptor 

func (*StructType) Descriptor() ([]byte, []int)

func (*StructType) GetFields 

func (m *StructType) GetFields() map[string]*DataType

func (*StructType) ProtoMessage 

func (*StructType) ProtoMessage()

func (*StructType) Reset 

func (m *StructType) Reset()

func (*StructType) String 

func (m *StructType) String() string

func (*StructType) XXX_DiscardUnknown 

func (m *StructType) XXX_DiscardUnknown()

func (*StructType) XXX_Marshal 

func (m *StructType) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*StructType) XXX_Merge 

func (m *StructType) XXX_Merge(src proto.Message)

func (*StructType) XXX_Size 

func (m *StructType) XXX_Size() int

func (*StructType) XXX_Unmarshal 

func (m *StructType) XXX_Unmarshal(b []byte) error

type TableSpec 

type TableSpec struct {
	// Output only. The resource name of the table spec.
	// Form:
	//
	// `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}`
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// column_spec_id of the time column. Only used if the parent dataset's
	// ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE
	// and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and
	// those in between to VALIDATE.
	// Required type: TIMESTAMP.
	// If both this column and ml_use_column are not set, then ML use of all rows
	// will be assigned by AutoML. NOTE: Updates of this field will instantly
	// affect any other users concurrently working with the dataset.
	TimeColumnSpecId string `protobuf:"bytes,2,opt,name=time_column_spec_id,json=timeColumnSpecId,proto3" json:"time_column_spec_id,omitempty"`
	// Output only. The number of rows (i.e. examples) in the table.
	RowCount int64 `protobuf:"varint,3,opt,name=row_count,json=rowCount,proto3" json:"row_count,omitempty"`
	// Output only. The number of valid rows (i.e. without values that don't match
	// DataType-s of their columns).
	ValidRowCount int64 `protobuf:"varint,4,opt,name=valid_row_count,json=validRowCount,proto3" json:"valid_row_count,omitempty"`
	// Output only. The number of columns of the table. That is, the number of
	// child ColumnSpec-s.
	ColumnCount int64 `protobuf:"varint,7,opt,name=column_count,json=columnCount,proto3" json:"column_count,omitempty"`
	// Output only. Input configs via which data currently residing in the table
	// had been imported.
	InputConfigs []*InputConfig `protobuf:"bytes,5,rep,name=input_configs,json=inputConfigs,proto3" json:"input_configs,omitempty"`
	// Used to perform consistent read-modify-write updates. If not set, a blind
	// "overwrite" update happens.
	Etag                 string   `protobuf:"bytes,6,opt,name=etag,proto3" json:"etag,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by:

  • Tables

func (*TableSpec) Descriptor 

func (*TableSpec) Descriptor() ([]byte, []int)

func (*TableSpec) GetColumnCount 

func (m *TableSpec) GetColumnCount() int64

func (*TableSpec) GetEtag 

func (m *TableSpec) GetEtag() string

func (*TableSpec) GetInputConfigs 

func (m *TableSpec) GetInputConfigs() []*InputConfig

func (*TableSpec) GetName 

func (m *TableSpec) GetName() string

func (*TableSpec) GetRowCount 

func (m *TableSpec) GetRowCount() int64

func (*TableSpec) GetTimeColumnSpecId 

func (m *TableSpec) GetTimeColumnSpecId() string

func (*TableSpec) GetValidRowCount 

func (m *TableSpec) GetValidRowCount() int64

func (*TableSpec) ProtoMessage 

func (*TableSpec) ProtoMessage()

func (*TableSpec) Reset 

func (m *TableSpec) Reset()

func (*TableSpec) String 

func (m *TableSpec) String() string

func (*TableSpec) XXX_DiscardUnknown 

func (m *TableSpec) XXX_DiscardUnknown()

func (*TableSpec) XXX_Marshal 

func (m *TableSpec) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TableSpec) XXX_Merge 

func (m *TableSpec) XXX_Merge(src proto.Message)

func (*TableSpec) XXX_Size 

func (m *TableSpec) XXX_Size() int

func (*TableSpec) XXX_Unmarshal 

func (m *TableSpec) XXX_Unmarshal(b []byte) error

type TablesAnnotation 

type TablesAnnotation struct {
	// Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher
	// value means greater confidence in the returned value.
	// For
	//
	// [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
	// of FLOAT64 data type the score is not populated.
	Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
	// Output only. Only populated when
	//
	// [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
	// has FLOAT64 data type. An interval in which the exactly correct target
	// value has 95% chance to be in.
	PredictionInterval *DoubleRange `protobuf:"bytes,4,opt,name=prediction_interval,json=predictionInterval,proto3" json:"prediction_interval,omitempty"`
	// The predicted value of the row's
	//
	// [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
	// The value depends on the column's DataType:
	//
	// * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
	//   value.
	//
	// * FLOAT64 - the predicted (with above `prediction_interval`) FLOAT64 value.
	Value *_struct.Value `protobuf:"bytes,2,opt,name=value,proto3" json:"value,omitempty"`
	// Output only. Auxiliary information for each of the model's
	//
	// [input_feature_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
	// with respect to this particular prediction.
	// If no other fields than
	//
	// [column_spec_name][google.cloud.automl.v1beta1.TablesModelColumnInfo.column_spec_name]
	// and
	//
	// [column_display_name][google.cloud.automl.v1beta1.TablesModelColumnInfo.column_display_name]
	// would be populated, then this whole field is not.
	TablesModelColumnInfo []*TablesModelColumnInfo `` /* 128-byte string literal not displayed */
	// Output only. Stores the prediction score for the baseline example, which
	// is defined as the example with all values set to their baseline values.
	// This is used as part of the Sampled Shapley explanation of the model's
	// prediction. This field is populated only when feature importance is
	// requested. For regression models, this holds the baseline prediction for
	// the baseline example. For classification models, this holds the baseline
	// prediction for the baseline example for the argmax class.
	BaselineScore        float32  `protobuf:"fixed32,5,opt,name=baseline_score,json=baselineScore,proto3" json:"baseline_score,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Contains annotation details specific to Tables.

func (*TablesAnnotation) Descriptor 

func (*TablesAnnotation) Descriptor() ([]byte, []int)

func (*TablesAnnotation) GetBaselineScore 

func (m *TablesAnnotation) GetBaselineScore() float32

func (*TablesAnnotation) GetPredictionInterval 

func (m *TablesAnnotation) GetPredictionInterval() *DoubleRange

func (*TablesAnnotation) GetScore 

func (m *TablesAnnotation) GetScore() float32

func (*TablesAnnotation) GetTablesModelColumnInfo 

func (m *TablesAnnotation) GetTablesModelColumnInfo() []*TablesModelColumnInfo

func (*TablesAnnotation) GetValue 

func (m *TablesAnnotation) GetValue() *_struct.Value

func (*TablesAnnotation) ProtoMessage 

func (*TablesAnnotation) ProtoMessage()

func (*TablesAnnotation) Reset 

func (m *TablesAnnotation) Reset()

func (*TablesAnnotation) String 

func (m *TablesAnnotation) String() string

func (*TablesAnnotation) XXX_DiscardUnknown 

func (m *TablesAnnotation) XXX_DiscardUnknown()

func (*TablesAnnotation) XXX_Marshal 

func (m *TablesAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TablesAnnotation) XXX_Merge 

func (m *TablesAnnotation) XXX_Merge(src proto.Message)

func (*TablesAnnotation) XXX_Size 

func (m *TablesAnnotation) XXX_Size() int

func (*TablesAnnotation) XXX_Unmarshal 

func (m *TablesAnnotation) XXX_Unmarshal(b []byte) error

type TablesDatasetMetadata 

type TablesDatasetMetadata struct {
	// Output only. The table_spec_id of the primary table of this dataset.
	PrimaryTableSpecId string `protobuf:"bytes,1,opt,name=primary_table_spec_id,json=primaryTableSpecId,proto3" json:"primary_table_spec_id,omitempty"`
	// column_spec_id of the primary table's column that should be used as the
	// training & prediction target.
	// This column must be non-nullable and have one of following data types
	// (otherwise model creation will error):
	//
	// * CATEGORY
	//
	// * FLOAT64
	//
	// If the type is CATEGORY , only up to
	// 100 unique values may exist in that column across all rows.
	//
	// NOTE: Updates of this field will instantly affect any other users
	// concurrently working with the dataset.
	TargetColumnSpecId string `protobuf:"bytes,2,opt,name=target_column_spec_id,json=targetColumnSpecId,proto3" json:"target_column_spec_id,omitempty"`
	// column_spec_id of the primary table's column that should be used as the
	// weight column, i.e. the higher the value the more important the row will be
	// during model training.
	// Required type: FLOAT64.
	// Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
	//                 ignored for training.
	// If not set all rows are assumed to have equal weight of 1.
	// NOTE: Updates of this field will instantly affect any other users
	// concurrently working with the dataset.
	WeightColumnSpecId string `protobuf:"bytes,3,opt,name=weight_column_spec_id,json=weightColumnSpecId,proto3" json:"weight_column_spec_id,omitempty"`
	// column_spec_id of the primary table column which specifies a possible ML
	// use of the row, i.e. the column will be used to split the rows into TRAIN,
	// VALIDATE and TEST sets.
	// Required type: STRING.
	// This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
	// among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
	// case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
	// that if a given ml use distribution makes it impossible to create a "good"
	// model, that call will error describing the issue.
	// If both this column_spec_id and primary table's time_column_spec_id are not
	// set, then all rows are treated as `UNASSIGNED`.
	// NOTE: Updates of this field will instantly affect any other users
	// concurrently working with the dataset.
	MlUseColumnSpecId string `protobuf:"bytes,4,opt,name=ml_use_column_spec_id,json=mlUseColumnSpecId,proto3" json:"ml_use_column_spec_id,omitempty"`
	// Output only. Correlations between
	//
	// [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
	// and other columns of the
	//
	// [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
	// Only set if the target column is set. Mapping from other column spec id to
	// its CorrelationStats with the target column.
	// This field may be stale, see the stats_update_time field for
	// for the timestamp at which these stats were last updated.
	TargetColumnCorrelations map[string]*CorrelationStats `` /* 223-byte string literal not displayed */
	// Output only. The most recent timestamp when target_column_correlations
	// field and all descendant ColumnSpec.data_stats and
	// ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
	// changes that happened to the dataset afterwards are not reflected in these
	// fields values. The regeneration happens in the background on a best effort
	// basis.
	StatsUpdateTime      *timestamp.Timestamp `protobuf:"bytes,7,opt,name=stats_update_time,json=statsUpdateTime,proto3" json:"stats_update_time,omitempty"`
	XXX_NoUnkeyedLiteral struct{}             `json:"-"`
	XXX_unrecognized     []byte               `json:"-"`
	XXX_sizecache        int32                `json:"-"`
}

Metadata for a dataset used for AutoML Tables.

func (*TablesDatasetMetadata) Descriptor 

func (*TablesDatasetMetadata) Descriptor() ([]byte, []int)

func (*TablesDatasetMetadata) GetMlUseColumnSpecId 

func (m *TablesDatasetMetadata) GetMlUseColumnSpecId() string

func (*TablesDatasetMetadata) GetPrimaryTableSpecId 

func (m *TablesDatasetMetadata) GetPrimaryTableSpecId() string

func (*TablesDatasetMetadata) GetStatsUpdateTime 

func (m *TablesDatasetMetadata) GetStatsUpdateTime() *timestamp.Timestamp

func (*TablesDatasetMetadata) GetTargetColumnCorrelations 

func (m *TablesDatasetMetadata) GetTargetColumnCorrelations() map[string]*CorrelationStats

func (*TablesDatasetMetadata) GetTargetColumnSpecId 

func (m *TablesDatasetMetadata) GetTargetColumnSpecId() string

func (*TablesDatasetMetadata) GetWeightColumnSpecId 

func (m *TablesDatasetMetadata) GetWeightColumnSpecId() string

func (*TablesDatasetMetadata) ProtoMessage 

func (*TablesDatasetMetadata) ProtoMessage()

func (*TablesDatasetMetadata) Reset 

func (m *TablesDatasetMetadata) Reset()

func (*TablesDatasetMetadata) String 

func (m *TablesDatasetMetadata) String() string

func (*TablesDatasetMetadata) XXX_DiscardUnknown 

func (m *TablesDatasetMetadata) XXX_DiscardUnknown()

func (*TablesDatasetMetadata) XXX_Marshal 

func (m *TablesDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TablesDatasetMetadata) XXX_Merge 

func (m *TablesDatasetMetadata) XXX_Merge(src proto.Message)

func (*TablesDatasetMetadata) XXX_Size 

func (m *TablesDatasetMetadata) XXX_Size() int

func (*TablesDatasetMetadata) XXX_Unmarshal 

func (m *TablesDatasetMetadata) XXX_Unmarshal(b []byte) error

type TablesModelColumnInfo 

type TablesModelColumnInfo struct {
	// Output only. The name of the ColumnSpec describing the column. Not
	// populated when this proto is outputted to BigQuery.
	ColumnSpecName string `protobuf:"bytes,1,opt,name=column_spec_name,json=columnSpecName,proto3" json:"column_spec_name,omitempty"`
	// Output only. The display name of the column (same as the display_name of
	// its ColumnSpec).
	ColumnDisplayName string `protobuf:"bytes,2,opt,name=column_display_name,json=columnDisplayName,proto3" json:"column_display_name,omitempty"`
	// Output only. When given as part of a Model (always populated):
	// Measurement of how much model predictions correctness on the TEST data
	// depend on values in this column. A value between 0 and 1, higher means
	// higher influence. These values are normalized - for all input feature
	// columns of a given model they add to 1.
	//
	// When given back by Predict (populated iff
	// [feature_importance
	// param][google.cloud.automl.v1beta1.PredictRequest.params] is set) or Batch
	// Predict (populated iff
	// [feature_importance][google.cloud.automl.v1beta1.PredictRequest.params]
	// param is set):
	// Measurement of how impactful for the prediction returned for the given row
	// the value in this column was. Specifically, the feature importance
	// specifies the marginal contribution that the feature made to the prediction
	// score compared to the baseline score. These values are computed using the
	// Sampled Shapley method.
	FeatureImportance    float32  `protobuf:"fixed32,3,opt,name=feature_importance,json=featureImportance,proto3" json:"feature_importance,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

An information specific to given column and Tables Model, in context of the Model and the predictions created by it.

func (*TablesModelColumnInfo) Descriptor 

func (*TablesModelColumnInfo) Descriptor() ([]byte, []int)

func (*TablesModelColumnInfo) GetColumnDisplayName 

func (m *TablesModelColumnInfo) GetColumnDisplayName() string

func (*TablesModelColumnInfo) GetColumnSpecName 

func (m *TablesModelColumnInfo) GetColumnSpecName() string

func (*TablesModelColumnInfo) GetFeatureImportance 

func (m *TablesModelColumnInfo) GetFeatureImportance() float32

func (*TablesModelColumnInfo) ProtoMessage 

func (*TablesModelColumnInfo) ProtoMessage()

func (*TablesModelColumnInfo) Reset 

func (m *TablesModelColumnInfo) Reset()

func (*TablesModelColumnInfo) String 

func (m *TablesModelColumnInfo) String() string

func (*TablesModelColumnInfo) XXX_DiscardUnknown 

func (m *TablesModelColumnInfo) XXX_DiscardUnknown()

func (*TablesModelColumnInfo) XXX_Marshal 

func (m *TablesModelColumnInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TablesModelColumnInfo) XXX_Merge 

func (m *TablesModelColumnInfo) XXX_Merge(src proto.Message)

func (*TablesModelColumnInfo) XXX_Size 

func (m *TablesModelColumnInfo) XXX_Size() int

func (*TablesModelColumnInfo) XXX_Unmarshal 

func (m *TablesModelColumnInfo) XXX_Unmarshal(b []byte) error

type TablesModelMetadata 

type TablesModelMetadata struct {
	// Additional optimization objective configuration. Required for
	// `MAXIMIZE_PRECISION_AT_RECALL` and `MAXIMIZE_RECALL_AT_PRECISION`,
	// otherwise unused.
	//
	// Types that are valid to be assigned to AdditionalOptimizationObjectiveConfig:
	//	*TablesModelMetadata_OptimizationObjectiveRecallValue
	//	*TablesModelMetadata_OptimizationObjectivePrecisionValue
	AdditionalOptimizationObjectiveConfig isTablesModelMetadata_AdditionalOptimizationObjectiveConfig `protobuf_oneof:"additional_optimization_objective_config"`
	// Column spec of the dataset's primary table's column the model is
	// predicting. Snapshotted when model creation started.
	// Only 3 fields are used:
	// name - May be set on CreateModel, if it's not then the ColumnSpec
	//        corresponding to the current target_column_spec_id of the dataset
	//        the model is trained from is used.
	//        If neither is set, CreateModel will error.
	// display_name - Output only.
	// data_type - Output only.
	TargetColumnSpec *ColumnSpec `protobuf:"bytes,2,opt,name=target_column_spec,json=targetColumnSpec,proto3" json:"target_column_spec,omitempty"`
	// Column specs of the dataset's primary table's columns, on which
	// the model is trained and which are used as the input for predictions.
	// The
	//
	// [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
	// as well as, according to dataset's state upon model creation,
	//
	// [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
	// and
	//
	// [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
	// must never be included here.
	//
	// Only 3 fields are used:
	//
	// * name - May be set on CreateModel, if set only the columns specified are
	//   used, otherwise all primary table's columns (except the ones listed
	//   above) are used for the training and prediction input.
	//
	// * display_name - Output only.
	//
	// * data_type - Output only.
	InputFeatureColumnSpecs []*ColumnSpec `` /* 134-byte string literal not displayed */
	// Objective function the model is optimizing towards. The training process
	// creates a model that maximizes/minimizes the value of the objective
	// function over the validation set.
	//
	// The supported optimization objectives depend on the prediction type.
	// If the field is not set, a default objective function is used.
	//
	// CLASSIFICATION_BINARY:
	//   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
	//                                 operating characteristic (ROC) curve.
	//   "MINIMIZE_LOG_LOSS" - Minimize log loss.
	//   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
	//   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
	//                                   recall value.
	//   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
	//                                    precision value.
	//
	// CLASSIFICATION_MULTI_CLASS :
	//   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
	//
	//
	// REGRESSION:
	//   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
	//   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
	//   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
	OptimizationObjective string `protobuf:"bytes,4,opt,name=optimization_objective,json=optimizationObjective,proto3" json:"optimization_objective,omitempty"`
	// Output only. Auxiliary information for each of the
	// input_feature_column_specs with respect to this particular model.
	TablesModelColumnInfo []*TablesModelColumnInfo `` /* 128-byte string literal not displayed */
	// Required. The train budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour.
	//
	// The training cost of the model will not exceed this budget. The final cost
	// will be attempted to be close to the budget, though may end up being (even)
	// noticeably smaller - at the backend's discretion. This especially may
	// happen when further model training ceases to provide any improvements.
	//
	// If the budget is set to a value known to be insufficient to train a
	// model for the given dataset, the training won't be attempted and
	// will error.
	//
	// The train budget must be between 1,000 and 72,000 milli node hours,
	// inclusive.
	TrainBudgetMilliNodeHours int64 `` /* 143-byte string literal not displayed */
	// Output only. The actual training cost of the model, expressed in milli
	// node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
	// to not exceed the train budget.
	TrainCostMilliNodeHours int64 `` /* 137-byte string literal not displayed */
	// Use the entire training budget. This disables the early stopping feature.
	// By default, the early stopping feature is enabled, which means that AutoML
	// Tables might stop training before the entire training budget has been used.
	DisableEarlyStopping bool     `protobuf:"varint,12,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model metadata specific to AutoML Tables.

func (*TablesModelMetadata) Descriptor 

func (*TablesModelMetadata) Descriptor() ([]byte, []int)

func (*TablesModelMetadata) GetAdditionalOptimizationObjectiveConfig 

func (m *TablesModelMetadata) GetAdditionalOptimizationObjectiveConfig() isTablesModelMetadata_AdditionalOptimizationObjectiveConfig

func (*TablesModelMetadata) GetDisableEarlyStopping 

func (m *TablesModelMetadata) GetDisableEarlyStopping() bool

func (*TablesModelMetadata) GetInputFeatureColumnSpecs 

func (m *TablesModelMetadata) GetInputFeatureColumnSpecs() []*ColumnSpec

func (*TablesModelMetadata) GetOptimizationObjective 

func (m *TablesModelMetadata) GetOptimizationObjective() string

func (*TablesModelMetadata) GetOptimizationObjectivePrecisionValue 

func (m *TablesModelMetadata) GetOptimizationObjectivePrecisionValue() float32

func (*TablesModelMetadata) GetOptimizationObjectiveRecallValue 

func (m *TablesModelMetadata) GetOptimizationObjectiveRecallValue() float32

func (*TablesModelMetadata) GetTablesModelColumnInfo 

func (m *TablesModelMetadata) GetTablesModelColumnInfo() []*TablesModelColumnInfo

func (*TablesModelMetadata) GetTargetColumnSpec 

func (m *TablesModelMetadata) GetTargetColumnSpec() *ColumnSpec

func (*TablesModelMetadata) GetTrainBudgetMilliNodeHours 

func (m *TablesModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*TablesModelMetadata) GetTrainCostMilliNodeHours 

func (m *TablesModelMetadata) GetTrainCostMilliNodeHours() int64

func (*TablesModelMetadata) ProtoMessage 

func (*TablesModelMetadata) ProtoMessage()

func (*TablesModelMetadata) Reset 

func (m *TablesModelMetadata) Reset()

func (*TablesModelMetadata) String 

func (m *TablesModelMetadata) String() string

func (*TablesModelMetadata) XXX_DiscardUnknown 

func (m *TablesModelMetadata) XXX_DiscardUnknown()

func (*TablesModelMetadata) XXX_Marshal 

func (m *TablesModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TablesModelMetadata) XXX_Merge 

func (m *TablesModelMetadata) XXX_Merge(src proto.Message)

func (*TablesModelMetadata) XXX_OneofWrappers 

func (*TablesModelMetadata) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*TablesModelMetadata) XXX_Size 

func (m *TablesModelMetadata) XXX_Size() int

func (*TablesModelMetadata) XXX_Unmarshal 

func (m *TablesModelMetadata) XXX_Unmarshal(b []byte) error

type TablesModelMetadata_OptimizationObjectivePrecisionValue 

type TablesModelMetadata_OptimizationObjectivePrecisionValue struct {
	OptimizationObjectivePrecisionValue float32 `protobuf:"fixed32,18,opt,name=optimization_objective_precision_value,json=optimizationObjectivePrecisionValue,proto3,oneof"`
}

type TablesModelMetadata_OptimizationObjectiveRecallValue 

type TablesModelMetadata_OptimizationObjectiveRecallValue struct {
	OptimizationObjectiveRecallValue float32 `protobuf:"fixed32,17,opt,name=optimization_objective_recall_value,json=optimizationObjectiveRecallValue,proto3,oneof"`
}

type TextClassificationDatasetMetadata 

type TextClassificationDatasetMetadata struct {
	// Required. Type of the classification problem.
	ClassificationType   ClassificationType `` /* 168-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}           `json:"-"`
	XXX_unrecognized     []byte             `json:"-"`
	XXX_sizecache        int32              `json:"-"`
}

Dataset metadata for classification.

func (*TextClassificationDatasetMetadata) Descriptor 

func (*TextClassificationDatasetMetadata) Descriptor() ([]byte, []int)

func (*TextClassificationDatasetMetadata) GetClassificationType 

func (m *TextClassificationDatasetMetadata) GetClassificationType() ClassificationType

func (*TextClassificationDatasetMetadata) ProtoMessage 

func (*TextClassificationDatasetMetadata) ProtoMessage()

func (*TextClassificationDatasetMetadata) Reset 

func (m *TextClassificationDatasetMetadata) Reset()

func (*TextClassificationDatasetMetadata) String 

func (m *TextClassificationDatasetMetadata) String() string

func (*TextClassificationDatasetMetadata) XXX_DiscardUnknown 

func (m *TextClassificationDatasetMetadata) XXX_DiscardUnknown()

func (*TextClassificationDatasetMetadata) XXX_Marshal 

func (m *TextClassificationDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextClassificationDatasetMetadata) XXX_Merge 

func (m *TextClassificationDatasetMetadata) XXX_Merge(src proto.Message)

func (*TextClassificationDatasetMetadata) XXX_Size 

func (m *TextClassificationDatasetMetadata) XXX_Size() int

func (*TextClassificationDatasetMetadata) XXX_Unmarshal 

func (m *TextClassificationDatasetMetadata) XXX_Unmarshal(b []byte) error

type TextClassificationModelMetadata 

type TextClassificationModelMetadata struct {
	// Output only. Classification type of the dataset used to train this model.
	ClassificationType   ClassificationType `` /* 168-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}           `json:"-"`
	XXX_unrecognized     []byte             `json:"-"`
	XXX_sizecache        int32              `json:"-"`
}

Model metadata that is specific to text classification.

func (*TextClassificationModelMetadata) Descriptor 

func (*TextClassificationModelMetadata) Descriptor() ([]byte, []int)

func (*TextClassificationModelMetadata) GetClassificationType 

func (m *TextClassificationModelMetadata) GetClassificationType() ClassificationType

func (*TextClassificationModelMetadata) ProtoMessage 

func (*TextClassificationModelMetadata) ProtoMessage()

func (*TextClassificationModelMetadata) Reset 

func (m *TextClassificationModelMetadata) Reset()

func (*TextClassificationModelMetadata) String 

func (m *TextClassificationModelMetadata) String() string

func (*TextClassificationModelMetadata) XXX_DiscardUnknown 

func (m *TextClassificationModelMetadata) XXX_DiscardUnknown()

func (*TextClassificationModelMetadata) XXX_Marshal 

func (m *TextClassificationModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextClassificationModelMetadata) XXX_Merge 

func (m *TextClassificationModelMetadata) XXX_Merge(src proto.Message)

func (*TextClassificationModelMetadata) XXX_Size 

func (m *TextClassificationModelMetadata) XXX_Size() int

func (*TextClassificationModelMetadata) XXX_Unmarshal 

func (m *TextClassificationModelMetadata) XXX_Unmarshal(b []byte) error

type TextExtractionAnnotation 

type TextExtractionAnnotation struct {
	// Required. Text extraction annotations can either be a text segment or a
	// text relation.
	//
	// Types that are valid to be assigned to Annotation:
	//	*TextExtractionAnnotation_TextSegment
	Annotation isTextExtractionAnnotation_Annotation `protobuf_oneof:"annotation"`
	// Output only. A confidence estimate between 0.0 and 1.0. A higher value
	// means greater confidence in correctness of the annotation.
	Score                float32  `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Annotation for identifying spans of text.

func (*TextExtractionAnnotation) Descriptor 

func (*TextExtractionAnnotation) Descriptor() ([]byte, []int)

func (*TextExtractionAnnotation) GetAnnotation 

func (m *TextExtractionAnnotation) GetAnnotation() isTextExtractionAnnotation_Annotation

func (*TextExtractionAnnotation) GetScore 

func (m *TextExtractionAnnotation) GetScore() float32

func (*TextExtractionAnnotation) GetTextSegment 

func (m *TextExtractionAnnotation) GetTextSegment() *TextSegment

func (*TextExtractionAnnotation) ProtoMessage 

func (*TextExtractionAnnotation) ProtoMessage()

func (*TextExtractionAnnotation) Reset 

func (m *TextExtractionAnnotation) Reset()

func (*TextExtractionAnnotation) String 

func (m *TextExtractionAnnotation) String() string

func (*TextExtractionAnnotation) XXX_DiscardUnknown 

func (m *TextExtractionAnnotation) XXX_DiscardUnknown()

func (*TextExtractionAnnotation) XXX_Marshal 

func (m *TextExtractionAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextExtractionAnnotation) XXX_Merge 

func (m *TextExtractionAnnotation) XXX_Merge(src proto.Message)

func (*TextExtractionAnnotation) XXX_OneofWrappers 

func (*TextExtractionAnnotation) XXX_OneofWrappers() []interface{}

XXX_OneofWrappers is for the internal use of the proto package.

func (*TextExtractionAnnotation) XXX_Size 

func (m *TextExtractionAnnotation) XXX_Size() int

func (*TextExtractionAnnotation) XXX_Unmarshal 

func (m *TextExtractionAnnotation) XXX_Unmarshal(b []byte) error

type TextExtractionAnnotation_TextSegment 

type TextExtractionAnnotation_TextSegment struct {
	TextSegment *TextSegment `protobuf:"bytes,3,opt,name=text_segment,json=textSegment,proto3,oneof"`
}

type TextExtractionDatasetMetadata 

type TextExtractionDatasetMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Dataset metadata that is specific to text extraction

func (*TextExtractionDatasetMetadata) Descriptor 

func (*TextExtractionDatasetMetadata) Descriptor() ([]byte, []int)

func (*TextExtractionDatasetMetadata) ProtoMessage 

func (*TextExtractionDatasetMetadata) ProtoMessage()

func (*TextExtractionDatasetMetadata) Reset 

func (m *TextExtractionDatasetMetadata) Reset()

func (*TextExtractionDatasetMetadata) String 

func (m *TextExtractionDatasetMetadata) String() string

func (*TextExtractionDatasetMetadata) XXX_DiscardUnknown 

func (m *TextExtractionDatasetMetadata) XXX_DiscardUnknown()

func (*TextExtractionDatasetMetadata) XXX_Marshal 

func (m *TextExtractionDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextExtractionDatasetMetadata) XXX_Merge 

func (m *TextExtractionDatasetMetadata) XXX_Merge(src proto.Message)

func (*TextExtractionDatasetMetadata) XXX_Size 

func (m *TextExtractionDatasetMetadata) XXX_Size() int

func (*TextExtractionDatasetMetadata) XXX_Unmarshal 

func (m *TextExtractionDatasetMetadata) XXX_Unmarshal(b []byte) error

type TextExtractionEvaluationMetrics 

type TextExtractionEvaluationMetrics struct {
	// Output only. The Area under precision recall curve metric.
	AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`
	// Output only. Metrics that have confidence thresholds.
	// Precision-recall curve can be derived from it.
	ConfidenceMetricsEntries []*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry `` /* 135-byte string literal not displayed */
	XXX_NoUnkeyedLiteral     struct{}                                                  `json:"-"`
	XXX_unrecognized         []byte                                                    `json:"-"`
	XXX_sizecache            int32                                                     `json:"-"`
}

Model evaluation metrics for text extraction problems.

func (*TextExtractionEvaluationMetrics) Descriptor 

func (*TextExtractionEvaluationMetrics) Descriptor() ([]byte, []int)

func (*TextExtractionEvaluationMetrics) GetAuPrc 

func (m *TextExtractionEvaluationMetrics) GetAuPrc() float32

func (*TextExtractionEvaluationMetrics) GetConfidenceMetricsEntries 

func (m *TextExtractionEvaluationMetrics) GetConfidenceMetricsEntries() []*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry

func (*TextExtractionEvaluationMetrics) ProtoMessage 

func (*TextExtractionEvaluationMetrics) ProtoMessage()

func (*TextExtractionEvaluationMetrics) Reset 

func (m *TextExtractionEvaluationMetrics) Reset()

func (*TextExtractionEvaluationMetrics) String 

func (m *TextExtractionEvaluationMetrics) String() string

func (*TextExtractionEvaluationMetrics) XXX_DiscardUnknown 

func (m *TextExtractionEvaluationMetrics) XXX_DiscardUnknown()

func (*TextExtractionEvaluationMetrics) XXX_Marshal 

func (m *TextExtractionEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextExtractionEvaluationMetrics) XXX_Merge 

func (m *TextExtractionEvaluationMetrics) XXX_Merge(src proto.Message)

func (*TextExtractionEvaluationMetrics) XXX_Size 

func (m *TextExtractionEvaluationMetrics) XXX_Size() int

func (*TextExtractionEvaluationMetrics) XXX_Unmarshal 

func (m *TextExtractionEvaluationMetrics) XXX_Unmarshal(b []byte) error

type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry 

type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry struct {
	// Output only. The confidence threshold value used to compute the metrics.
	// Only annotations with score of at least this threshold are considered to
	// be ones the model would return.
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
	// Output only. Recall under the given confidence threshold.
	Recall float32 `protobuf:"fixed32,3,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. Precision under the given confidence threshold.
	Precision float32 `protobuf:"fixed32,4,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score              float32  `protobuf:"fixed32,5,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Metrics for a single confidence threshold.

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Descriptor 

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetRecall 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage 

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Reset 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Reset()

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) String 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) String() string

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_DiscardUnknown 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_DiscardUnknown()

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_Marshal 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_Merge 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_Merge(src proto.Message)

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_Size 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_Size() int

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_Unmarshal 

func (m *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) XXX_Unmarshal(b []byte) error

type TextExtractionModelMetadata 

type TextExtractionModelMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model metadata that is specific to text extraction.

func (*TextExtractionModelMetadata) Descriptor 

func (*TextExtractionModelMetadata) Descriptor() ([]byte, []int)

func (*TextExtractionModelMetadata) ProtoMessage 

func (*TextExtractionModelMetadata) ProtoMessage()

func (*TextExtractionModelMetadata) Reset 

func (m *TextExtractionModelMetadata) Reset()

func (*TextExtractionModelMetadata) String 

func (m *TextExtractionModelMetadata) String() string

func (*TextExtractionModelMetadata) XXX_DiscardUnknown 

func (m *TextExtractionModelMetadata) XXX_DiscardUnknown()

func (*TextExtractionModelMetadata) XXX_Marshal 

func (m *TextExtractionModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextExtractionModelMetadata) XXX_Merge 

func (m *TextExtractionModelMetadata) XXX_Merge(src proto.Message)

func (*TextExtractionModelMetadata) XXX_Size 

func (m *TextExtractionModelMetadata) XXX_Size() int

func (*TextExtractionModelMetadata) XXX_Unmarshal 

func (m *TextExtractionModelMetadata) XXX_Unmarshal(b []byte) error

type TextSegment 

type TextSegment struct {
	// Output only. The content of the TextSegment.
	Content string `protobuf:"bytes,3,opt,name=content,proto3" json:"content,omitempty"`
	// Required. Zero-based character index of the first character of the text
	// segment (counting characters from the beginning of the text).
	StartOffset int64 `protobuf:"varint,1,opt,name=start_offset,json=startOffset,proto3" json:"start_offset,omitempty"`
	// Required. Zero-based character index of the first character past the end of
	// the text segment (counting character from the beginning of the text).
	// The character at the end_offset is NOT included in the text segment.
	EndOffset            int64    `protobuf:"varint,2,opt,name=end_offset,json=endOffset,proto3" json:"end_offset,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.

func (*TextSegment) Descriptor 

func (*TextSegment) Descriptor() ([]byte, []int)

func (*TextSegment) GetContent 

func (m *TextSegment) GetContent() string

func (*TextSegment) GetEndOffset 

func (m *TextSegment) GetEndOffset() int64

func (*TextSegment) GetStartOffset 

func (m *TextSegment) GetStartOffset() int64

func (*TextSegment) ProtoMessage 

func (*TextSegment) ProtoMessage()

func (*TextSegment) Reset 

func (m *TextSegment) Reset()

func (*TextSegment) String 

func (m *TextSegment) String() string

func (*TextSegment) XXX_DiscardUnknown 

func (m *TextSegment) XXX_DiscardUnknown()

func (*TextSegment) XXX_Marshal 

func (m *TextSegment) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextSegment) XXX_Merge 

func (m *TextSegment) XXX_Merge(src proto.Message)

func (*TextSegment) XXX_Size 

func (m *TextSegment) XXX_Size() int

func (*TextSegment) XXX_Unmarshal 

func (m *TextSegment) XXX_Unmarshal(b []byte) error

type TextSentimentAnnotation 

type TextSentimentAnnotation struct {
	// Output only. The sentiment with the semantic, as given to the
	// [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData] when populating the dataset from which the model used
	// for the prediction had been trained.
	// The sentiment values are between 0 and
	// Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive),
	// with higher value meaning more positive sentiment. They are completely
	// relative, i.e. 0 means least positive sentiment and sentiment_max means
	// the most positive from the sentiments present in the train data. Therefore
	//  e.g. if train data had only negative sentiment, then sentiment_max, would
	// be still negative (although least negative).
	// The sentiment shouldn't be confused with "score" or "magnitude"
	// from the previous Natural Language Sentiment Analysis API.
	Sentiment            int32    `protobuf:"varint,1,opt,name=sentiment,proto3" json:"sentiment,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Contains annotation details specific to text sentiment.

func (*TextSentimentAnnotation) Descriptor 

func (*TextSentimentAnnotation) Descriptor() ([]byte, []int)

func (*TextSentimentAnnotation) GetSentiment 

func (m *TextSentimentAnnotation) GetSentiment() int32

func (*TextSentimentAnnotation) ProtoMessage 

func (*TextSentimentAnnotation) ProtoMessage()

func (*TextSentimentAnnotation) Reset 

func (m *TextSentimentAnnotation) Reset()

func (*TextSentimentAnnotation) String 

func (m *TextSentimentAnnotation) String() string

func (*TextSentimentAnnotation) XXX_DiscardUnknown 

func (m *TextSentimentAnnotation) XXX_DiscardUnknown()

func (*TextSentimentAnnotation) XXX_Marshal 

func (m *TextSentimentAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextSentimentAnnotation) XXX_Merge 

func (m *TextSentimentAnnotation) XXX_Merge(src proto.Message)

func (*TextSentimentAnnotation) XXX_Size 

func (m *TextSentimentAnnotation) XXX_Size() int

func (*TextSentimentAnnotation) XXX_Unmarshal 

func (m *TextSentimentAnnotation) XXX_Unmarshal(b []byte) error

type TextSentimentDatasetMetadata 

type TextSentimentDatasetMetadata struct {
	// Required. A sentiment is expressed as an integer ordinal, where higher value
	// means a more positive sentiment. The range of sentiments that will be used
	// is between 0 and sentiment_max (inclusive on both ends), and all the values
	// in the range must be represented in the dataset before a model can be
	// created.
	// sentiment_max value must be between 1 and 10 (inclusive).
	SentimentMax         int32    `protobuf:"varint,1,opt,name=sentiment_max,json=sentimentMax,proto3" json:"sentiment_max,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Dataset metadata for text sentiment.

func (*TextSentimentDatasetMetadata) Descriptor 

func (*TextSentimentDatasetMetadata) Descriptor() ([]byte, []int)

func (*TextSentimentDatasetMetadata) GetSentimentMax 

func (m *TextSentimentDatasetMetadata) GetSentimentMax() int32

func (*TextSentimentDatasetMetadata) ProtoMessage 

func (*TextSentimentDatasetMetadata) ProtoMessage()

func (*TextSentimentDatasetMetadata) Reset 

func (m *TextSentimentDatasetMetadata) Reset()

func (*TextSentimentDatasetMetadata) String 

func (m *TextSentimentDatasetMetadata) String() string

func (*TextSentimentDatasetMetadata) XXX_DiscardUnknown 

func (m *TextSentimentDatasetMetadata) XXX_DiscardUnknown()

func (*TextSentimentDatasetMetadata) XXX_Marshal 

func (m *TextSentimentDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextSentimentDatasetMetadata) XXX_Merge 

func (m *TextSentimentDatasetMetadata) XXX_Merge(src proto.Message)

func (*TextSentimentDatasetMetadata) XXX_Size 

func (m *TextSentimentDatasetMetadata) XXX_Size() int

func (*TextSentimentDatasetMetadata) XXX_Unmarshal 

func (m *TextSentimentDatasetMetadata) XXX_Unmarshal(b []byte) error

type TextSentimentEvaluationMetrics 

type TextSentimentEvaluationMetrics struct {
	// Output only. Precision.
	Precision float32 `protobuf:"fixed32,1,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. Recall.
	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score float32 `protobuf:"fixed32,3,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	// Output only. Mean absolute error. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	MeanAbsoluteError float32 `protobuf:"fixed32,4,opt,name=mean_absolute_error,json=meanAbsoluteError,proto3" json:"mean_absolute_error,omitempty"`
	// Output only. Mean squared error. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	MeanSquaredError float32 `protobuf:"fixed32,5,opt,name=mean_squared_error,json=meanSquaredError,proto3" json:"mean_squared_error,omitempty"`
	// Output only. Linear weighted kappa. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	LinearKappa float32 `protobuf:"fixed32,6,opt,name=linear_kappa,json=linearKappa,proto3" json:"linear_kappa,omitempty"`
	// Output only. Quadratic weighted kappa. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	QuadraticKappa float32 `protobuf:"fixed32,7,opt,name=quadratic_kappa,json=quadraticKappa,proto3" json:"quadratic_kappa,omitempty"`
	// Output only. Confusion matrix of the evaluation.
	// Only set for the overall model evaluation, not for evaluation of a single
	// annotation spec.
	ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,8,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`
	// Output only. The annotation spec ids used for this evaluation.
	// Deprecated .
	AnnotationSpecId     []string `protobuf:"bytes,9,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"` // Deprecated: Do not use.
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model evaluation metrics for text sentiment problems.

func (*TextSentimentEvaluationMetrics) Descriptor 

func (*TextSentimentEvaluationMetrics) Descriptor() ([]byte, []int)

func (*TextSentimentEvaluationMetrics) GetAnnotationSpecId deprecated 

func (m *TextSentimentEvaluationMetrics) GetAnnotationSpecId() []string

Deprecated: Do not use.

func (*TextSentimentEvaluationMetrics) GetConfusionMatrix 

func (m *TextSentimentEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix

func (*TextSentimentEvaluationMetrics) GetF1Score 

func (m *TextSentimentEvaluationMetrics) GetF1Score() float32

func (*TextSentimentEvaluationMetrics) GetLinearKappa 

func (m *TextSentimentEvaluationMetrics) GetLinearKappa() float32

func (*TextSentimentEvaluationMetrics) GetMeanAbsoluteError 

func (m *TextSentimentEvaluationMetrics) GetMeanAbsoluteError() float32

func (*TextSentimentEvaluationMetrics) GetMeanSquaredError 

func (m *TextSentimentEvaluationMetrics) GetMeanSquaredError() float32

func (*TextSentimentEvaluationMetrics) GetPrecision 

func (m *TextSentimentEvaluationMetrics) GetPrecision() float32

func (*TextSentimentEvaluationMetrics) GetQuadraticKappa 

func (m *TextSentimentEvaluationMetrics) GetQuadraticKappa() float32

func (*TextSentimentEvaluationMetrics) GetRecall 

func (m *TextSentimentEvaluationMetrics) GetRecall() float32

func (*TextSentimentEvaluationMetrics) ProtoMessage 

func (*TextSentimentEvaluationMetrics) ProtoMessage()

func (*TextSentimentEvaluationMetrics) Reset 

func (m *TextSentimentEvaluationMetrics) Reset()

func (*TextSentimentEvaluationMetrics) String 

func (m *TextSentimentEvaluationMetrics) String() string

func (*TextSentimentEvaluationMetrics) XXX_DiscardUnknown 

func (m *TextSentimentEvaluationMetrics) XXX_DiscardUnknown()

func (*TextSentimentEvaluationMetrics) XXX_Marshal 

func (m *TextSentimentEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextSentimentEvaluationMetrics) XXX_Merge 

func (m *TextSentimentEvaluationMetrics) XXX_Merge(src proto.Message)

func (*TextSentimentEvaluationMetrics) XXX_Size 

func (m *TextSentimentEvaluationMetrics) XXX_Size() int

func (*TextSentimentEvaluationMetrics) XXX_Unmarshal 

func (m *TextSentimentEvaluationMetrics) XXX_Unmarshal(b []byte) error

type TextSentimentModelMetadata 

type TextSentimentModelMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model metadata that is specific to text sentiment.

func (*TextSentimentModelMetadata) Descriptor 

func (*TextSentimentModelMetadata) Descriptor() ([]byte, []int)

func (*TextSentimentModelMetadata) ProtoMessage 

func (*TextSentimentModelMetadata) ProtoMessage()

func (*TextSentimentModelMetadata) Reset 

func (m *TextSentimentModelMetadata) Reset()

func (*TextSentimentModelMetadata) String 

func (m *TextSentimentModelMetadata) String() string

func (*TextSentimentModelMetadata) XXX_DiscardUnknown 

func (m *TextSentimentModelMetadata) XXX_DiscardUnknown()

func (*TextSentimentModelMetadata) XXX_Marshal 

func (m *TextSentimentModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextSentimentModelMetadata) XXX_Merge 

func (m *TextSentimentModelMetadata) XXX_Merge(src proto.Message)

func (*TextSentimentModelMetadata) XXX_Size 

func (m *TextSentimentModelMetadata) XXX_Size() int

func (*TextSentimentModelMetadata) XXX_Unmarshal 

func (m *TextSentimentModelMetadata) XXX_Unmarshal(b []byte) error

type TextSnippet 

type TextSnippet struct {
	// Required. The content of the text snippet as a string. Up to 250000
	// characters long.
	Content string `protobuf:"bytes,1,opt,name=content,proto3" json:"content,omitempty"`
	// Optional. The format of [content][google.cloud.automl.v1beta1.TextSnippet.content]. Currently the only two allowed
	// values are "text/html" and "text/plain". If left blank, the format is
	// automatically determined from the type of the uploaded [content][google.cloud.automl.v1beta1.TextSnippet.content].
	MimeType string `protobuf:"bytes,2,opt,name=mime_type,json=mimeType,proto3" json:"mime_type,omitempty"`
	// Output only. HTTP URI where you can download the content.
	ContentUri           string   `protobuf:"bytes,4,opt,name=content_uri,json=contentUri,proto3" json:"content_uri,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

A representation of a text snippet.

func (*TextSnippet) Descriptor 

func (*TextSnippet) Descriptor() ([]byte, []int)

func (*TextSnippet) GetContent 

func (m *TextSnippet) GetContent() string

func (*TextSnippet) GetContentUri 

func (m *TextSnippet) GetContentUri() string

func (*TextSnippet) GetMimeType 

func (m *TextSnippet) GetMimeType() string

func (*TextSnippet) ProtoMessage 

func (*TextSnippet) ProtoMessage()

func (*TextSnippet) Reset 

func (m *TextSnippet) Reset()

func (*TextSnippet) String 

func (m *TextSnippet) String() string

func (*TextSnippet) XXX_DiscardUnknown 

func (m *TextSnippet) XXX_DiscardUnknown()

func (*TextSnippet) XXX_Marshal 

func (m *TextSnippet) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TextSnippet) XXX_Merge 

func (m *TextSnippet) XXX_Merge(src proto.Message)

func (*TextSnippet) XXX_Size 

func (m *TextSnippet) XXX_Size() int

func (*TextSnippet) XXX_Unmarshal 

func (m *TextSnippet) XXX_Unmarshal(b []byte) error

type TimeSegment 

type TimeSegment struct {
	// Start of the time segment (inclusive), represented as the duration since
	// the example start.
	StartTimeOffset *duration.Duration `protobuf:"bytes,1,opt,name=start_time_offset,json=startTimeOffset,proto3" json:"start_time_offset,omitempty"`
	// End of the time segment (exclusive), represented as the duration since the
	// example start.
	EndTimeOffset        *duration.Duration `protobuf:"bytes,2,opt,name=end_time_offset,json=endTimeOffset,proto3" json:"end_time_offset,omitempty"`
	XXX_NoUnkeyedLiteral struct{}           `json:"-"`
	XXX_unrecognized     []byte             `json:"-"`
	XXX_sizecache        int32              `json:"-"`
}

A time period inside of an example that has a time dimension (e.g. video).

func (*TimeSegment) Descriptor 

func (*TimeSegment) Descriptor() ([]byte, []int)

func (*TimeSegment) GetEndTimeOffset 

func (m *TimeSegment) GetEndTimeOffset() *duration.Duration

func (*TimeSegment) GetStartTimeOffset 

func (m *TimeSegment) GetStartTimeOffset() *duration.Duration

func (*TimeSegment) ProtoMessage 

func (*TimeSegment) ProtoMessage()

func (*TimeSegment) Reset 

func (m *TimeSegment) Reset()

func (*TimeSegment) String 

func (m *TimeSegment) String() string

func (*TimeSegment) XXX_DiscardUnknown 

func (m *TimeSegment) XXX_DiscardUnknown()

func (*TimeSegment) XXX_Marshal 

func (m *TimeSegment) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TimeSegment) XXX_Merge 

func (m *TimeSegment) XXX_Merge(src proto.Message)

func (*TimeSegment) XXX_Size 

func (m *TimeSegment) XXX_Size() int

func (*TimeSegment) XXX_Unmarshal 

func (m *TimeSegment) XXX_Unmarshal(b []byte) error

type TimestampStats 

type TimestampStats struct {
	// The string key is the pre-defined granularity. Currently supported:
	// hour_of_day, day_of_week, month_of_year.
	// Granularities finer that the granularity of timestamp data are not
	// populated (e.g. if timestamps are at day granularity, then hour_of_day
	// is not populated).
	GranularStats        map[string]*TimestampStats_GranularStats `` /* 188-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}                                 `json:"-"`
	XXX_unrecognized     []byte                                   `json:"-"`
	XXX_sizecache        int32                                    `json:"-"`
}

The data statistics of a series of TIMESTAMP values.

func (*TimestampStats) Descriptor 

func (*TimestampStats) Descriptor() ([]byte, []int)

func (*TimestampStats) GetGranularStats 

func (m *TimestampStats) GetGranularStats() map[string]*TimestampStats_GranularStats

func (*TimestampStats) ProtoMessage 

func (*TimestampStats) ProtoMessage()

func (*TimestampStats) Reset 

func (m *TimestampStats) Reset()

func (*TimestampStats) String 

func (m *TimestampStats) String() string

func (*TimestampStats) XXX_DiscardUnknown 

func (m *TimestampStats) XXX_DiscardUnknown()

func (*TimestampStats) XXX_Marshal 

func (m *TimestampStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TimestampStats) XXX_Merge 

func (m *TimestampStats) XXX_Merge(src proto.Message)

func (*TimestampStats) XXX_Size 

func (m *TimestampStats) XXX_Size() int

func (*TimestampStats) XXX_Unmarshal 

func (m *TimestampStats) XXX_Unmarshal(b []byte) error

type TimestampStats_GranularStats 

type TimestampStats_GranularStats struct {
	// A map from granularity key to example count for that key.
	// E.g. for hour_of_day `13` means 1pm, or for month_of_year `5` means May).
	Buckets              map[int32]int64 `` /* 157-byte string literal not displayed */
	XXX_NoUnkeyedLiteral struct{}        `json:"-"`
	XXX_unrecognized     []byte          `json:"-"`
	XXX_sizecache        int32           `json:"-"`
}

Stats split by a defined in context granularity.

func (*TimestampStats_GranularStats) Descriptor 

func (*TimestampStats_GranularStats) Descriptor() ([]byte, []int)

func (*TimestampStats_GranularStats) GetBuckets 

func (m *TimestampStats_GranularStats) GetBuckets() map[int32]int64

func (*TimestampStats_GranularStats) ProtoMessage 

func (*TimestampStats_GranularStats) ProtoMessage()

func (*TimestampStats_GranularStats) Reset 

func (m *TimestampStats_GranularStats) Reset()

func (*TimestampStats_GranularStats) String 

func (m *TimestampStats_GranularStats) String() string

func (*TimestampStats_GranularStats) XXX_DiscardUnknown 

func (m *TimestampStats_GranularStats) XXX_DiscardUnknown()

func (*TimestampStats_GranularStats) XXX_Marshal 

func (m *TimestampStats_GranularStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TimestampStats_GranularStats) XXX_Merge 

func (m *TimestampStats_GranularStats) XXX_Merge(src proto.Message)

func (*TimestampStats_GranularStats) XXX_Size 

func (m *TimestampStats_GranularStats) XXX_Size() int

func (*TimestampStats_GranularStats) XXX_Unmarshal 

func (m *TimestampStats_GranularStats) XXX_Unmarshal(b []byte) error

type TranslationAnnotation 

type TranslationAnnotation struct {
	// Output only . The translated content.
	TranslatedContent    *TextSnippet `protobuf:"bytes,1,opt,name=translated_content,json=translatedContent,proto3" json:"translated_content,omitempty"`
	XXX_NoUnkeyedLiteral struct{}     `json:"-"`
	XXX_unrecognized     []byte       `json:"-"`
	XXX_sizecache        int32        `json:"-"`
}

Annotation details specific to translation.

func (*TranslationAnnotation) Descriptor 

func (*TranslationAnnotation) Descriptor() ([]byte, []int)

func (*TranslationAnnotation) GetTranslatedContent 

func (m *TranslationAnnotation) GetTranslatedContent() *TextSnippet

func (*TranslationAnnotation) ProtoMessage 

func (*TranslationAnnotation) ProtoMessage()

func (*TranslationAnnotation) Reset 

func (m *TranslationAnnotation) Reset()

func (*TranslationAnnotation) String 

func (m *TranslationAnnotation) String() string

func (*TranslationAnnotation) XXX_DiscardUnknown 

func (m *TranslationAnnotation) XXX_DiscardUnknown()

func (*TranslationAnnotation) XXX_Marshal 

func (m *TranslationAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TranslationAnnotation) XXX_Merge 

func (m *TranslationAnnotation) XXX_Merge(src proto.Message)

func (*TranslationAnnotation) XXX_Size 

func (m *TranslationAnnotation) XXX_Size() int

func (*TranslationAnnotation) XXX_Unmarshal 

func (m *TranslationAnnotation) XXX_Unmarshal(b []byte) error

type TranslationDatasetMetadata 

type TranslationDatasetMetadata struct {
	// Required. The BCP-47 language code of the source language.
	SourceLanguageCode string `protobuf:"bytes,1,opt,name=source_language_code,json=sourceLanguageCode,proto3" json:"source_language_code,omitempty"`
	// Required. The BCP-47 language code of the target language.
	TargetLanguageCode   string   `protobuf:"bytes,2,opt,name=target_language_code,json=targetLanguageCode,proto3" json:"target_language_code,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Dataset metadata that is specific to translation.

func (*TranslationDatasetMetadata) Descriptor 

func (*TranslationDatasetMetadata) Descriptor() ([]byte, []int)

func (*TranslationDatasetMetadata) GetSourceLanguageCode 

func (m *TranslationDatasetMetadata) GetSourceLanguageCode() string

func (*TranslationDatasetMetadata) GetTargetLanguageCode 

func (m *TranslationDatasetMetadata) GetTargetLanguageCode() string

func (*TranslationDatasetMetadata) ProtoMessage 

func (*TranslationDatasetMetadata) ProtoMessage()

func (*TranslationDatasetMetadata) Reset 

func (m *TranslationDatasetMetadata) Reset()

func (*TranslationDatasetMetadata) String 

func (m *TranslationDatasetMetadata) String() string

func (*TranslationDatasetMetadata) XXX_DiscardUnknown 

func (m *TranslationDatasetMetadata) XXX_DiscardUnknown()

func (*TranslationDatasetMetadata) XXX_Marshal 

func (m *TranslationDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TranslationDatasetMetadata) XXX_Merge 

func (m *TranslationDatasetMetadata) XXX_Merge(src proto.Message)

func (*TranslationDatasetMetadata) XXX_Size 

func (m *TranslationDatasetMetadata) XXX_Size() int

func (*TranslationDatasetMetadata) XXX_Unmarshal 

func (m *TranslationDatasetMetadata) XXX_Unmarshal(b []byte) error

type TranslationEvaluationMetrics 

type TranslationEvaluationMetrics struct {
	// Output only. BLEU score.
	BleuScore float64 `protobuf:"fixed64,1,opt,name=bleu_score,json=bleuScore,proto3" json:"bleu_score,omitempty"`
	// Output only. BLEU score for base model.
	BaseBleuScore        float64  `protobuf:"fixed64,2,opt,name=base_bleu_score,json=baseBleuScore,proto3" json:"base_bleu_score,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Evaluation metrics for the dataset.

func (*TranslationEvaluationMetrics) Descriptor 

func (*TranslationEvaluationMetrics) Descriptor() ([]byte, []int)

func (*TranslationEvaluationMetrics) GetBaseBleuScore 

func (m *TranslationEvaluationMetrics) GetBaseBleuScore() float64

func (*TranslationEvaluationMetrics) GetBleuScore 

func (m *TranslationEvaluationMetrics) GetBleuScore() float64

func (*TranslationEvaluationMetrics) ProtoMessage 

func (*TranslationEvaluationMetrics) ProtoMessage()

func (*TranslationEvaluationMetrics) Reset 

func (m *TranslationEvaluationMetrics) Reset()

func (*TranslationEvaluationMetrics) String 

func (m *TranslationEvaluationMetrics) String() string

func (*TranslationEvaluationMetrics) XXX_DiscardUnknown 

func (m *TranslationEvaluationMetrics) XXX_DiscardUnknown()

func (*TranslationEvaluationMetrics) XXX_Marshal 

func (m *TranslationEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TranslationEvaluationMetrics) XXX_Merge 

func (m *TranslationEvaluationMetrics) XXX_Merge(src proto.Message)

func (*TranslationEvaluationMetrics) XXX_Size 

func (m *TranslationEvaluationMetrics) XXX_Size() int

func (*TranslationEvaluationMetrics) XXX_Unmarshal 

func (m *TranslationEvaluationMetrics) XXX_Unmarshal(b []byte) error

type TranslationModelMetadata 

type TranslationModelMetadata struct {
	// The resource name of the model to use as a baseline to train the custom
	// model. If unset, we use the default base model provided by Google
	// Translate. Format:
	// `projects/{project_id}/locations/{location_id}/models/{model_id}`
	BaseModel string `protobuf:"bytes,1,opt,name=base_model,json=baseModel,proto3" json:"base_model,omitempty"`
	// Output only. Inferred from the dataset.
	// The source languge (The BCP-47 language code) that is used for training.
	SourceLanguageCode string `protobuf:"bytes,2,opt,name=source_language_code,json=sourceLanguageCode,proto3" json:"source_language_code,omitempty"`
	// Output only. The target languge (The BCP-47 language code) that is used for
	// training.
	TargetLanguageCode   string   `protobuf:"bytes,3,opt,name=target_language_code,json=targetLanguageCode,proto3" json:"target_language_code,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model metadata that is specific to translation.

func (*TranslationModelMetadata) Descriptor 

func (*TranslationModelMetadata) Descriptor() ([]byte, []int)

func (*TranslationModelMetadata) GetBaseModel 

func (m *TranslationModelMetadata) GetBaseModel() string

func (*TranslationModelMetadata) GetSourceLanguageCode 

func (m *TranslationModelMetadata) GetSourceLanguageCode() string

func (*TranslationModelMetadata) GetTargetLanguageCode 

func (m *TranslationModelMetadata) GetTargetLanguageCode() string

func (*TranslationModelMetadata) ProtoMessage 

func (*TranslationModelMetadata) ProtoMessage()

func (*TranslationModelMetadata) Reset 

func (m *TranslationModelMetadata) Reset()

func (*TranslationModelMetadata) String 

func (m *TranslationModelMetadata) String() string

func (*TranslationModelMetadata) XXX_DiscardUnknown 

func (m *TranslationModelMetadata) XXX_DiscardUnknown()

func (*TranslationModelMetadata) XXX_Marshal 

func (m *TranslationModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*TranslationModelMetadata) XXX_Merge 

func (m *TranslationModelMetadata) XXX_Merge(src proto.Message)

func (*TranslationModelMetadata) XXX_Size 

func (m *TranslationModelMetadata) XXX_Size() int

func (*TranslationModelMetadata) XXX_Unmarshal 

func (m *TranslationModelMetadata) XXX_Unmarshal(b []byte) error

type TypeCode 

type TypeCode int32

`TypeCode` is used as a part of DataType[google.cloud.automl.v1beta1.DataType]. 

const (
	// Not specified. Should not be used.
	TypeCode_TYPE_CODE_UNSPECIFIED TypeCode = 0
	// Encoded as `number`, or the strings `"NaN"`, `"Infinity"`, or
	// `"-Infinity"`.
	TypeCode_FLOAT64 TypeCode = 3
	// Must be between 0AD and 9999AD. Encoded as `string` according to
	// [time_format][google.cloud.automl.v1beta1.DataType.time_format], or, if
	// that format is not set, then in RFC 3339 `date-time` format, where
	// `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z).
	TypeCode_TIMESTAMP TypeCode = 4
	// Encoded as `string`.
	TypeCode_STRING TypeCode = 6
	// Encoded as `list`, where the list elements are represented according to
	//
	// [list_element_type][google.cloud.automl.v1beta1.DataType.list_element_type].
	TypeCode_ARRAY TypeCode = 8
	// Encoded as `struct`, where field values are represented according to
	// [struct_type][google.cloud.automl.v1beta1.DataType.struct_type].
	TypeCode_STRUCT TypeCode = 9
	// Values of this type are not further understood by AutoML,
	// e.g. AutoML is unable to tell the order of values (as it could with
	// FLOAT64), or is unable to say if one value contains another (as it
	// could with STRING).
	// Encoded as `string` (bytes should be base64-encoded, as described in RFC
	// 4648, section 4).
	TypeCode_CATEGORY TypeCode = 10
)

func (TypeCode) EnumDescriptor 

func (TypeCode) EnumDescriptor() ([]byte, []int)

func (TypeCode) String 

func (x TypeCode) String() string

type UndeployModelOperationMetadata 

type UndeployModelOperationMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Details of UndeployModel operation.

func (*UndeployModelOperationMetadata) Descriptor 

func (*UndeployModelOperationMetadata) Descriptor() ([]byte, []int)

func (*UndeployModelOperationMetadata) ProtoMessage 

func (*UndeployModelOperationMetadata) ProtoMessage()

func (*UndeployModelOperationMetadata) Reset 

func (m *UndeployModelOperationMetadata) Reset()

func (*UndeployModelOperationMetadata) String 

func (m *UndeployModelOperationMetadata) String() string

func (*UndeployModelOperationMetadata) XXX_DiscardUnknown 

func (m *UndeployModelOperationMetadata) XXX_DiscardUnknown()

func (*UndeployModelOperationMetadata) XXX_Marshal 

func (m *UndeployModelOperationMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*UndeployModelOperationMetadata) XXX_Merge 

func (m *UndeployModelOperationMetadata) XXX_Merge(src proto.Message)

func (*UndeployModelOperationMetadata) XXX_Size 

func (m *UndeployModelOperationMetadata) XXX_Size() int

func (*UndeployModelOperationMetadata) XXX_Unmarshal 

func (m *UndeployModelOperationMetadata) XXX_Unmarshal(b []byte) error

type UndeployModelRequest 

type UndeployModelRequest struct {
	// Required. Resource name of the model to undeploy.
	Name                 string   `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Request message for [AutoMl.UndeployModel][google.cloud.automl.v1beta1.AutoMl.UndeployModel].

func (*UndeployModelRequest) Descriptor 

func (*UndeployModelRequest) Descriptor() ([]byte, []int)

func (*UndeployModelRequest) GetName 

func (m *UndeployModelRequest) GetName() string

func (*UndeployModelRequest) ProtoMessage 

func (*UndeployModelRequest) ProtoMessage()

func (*UndeployModelRequest) Reset 

func (m *UndeployModelRequest) Reset()

func (*UndeployModelRequest) String 

func (m *UndeployModelRequest) String() string

func (*UndeployModelRequest) XXX_DiscardUnknown 

func (m *UndeployModelRequest) XXX_DiscardUnknown()

func (*UndeployModelRequest) XXX_Marshal 

func (m *UndeployModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*UndeployModelRequest) XXX_Merge 

func (m *UndeployModelRequest) XXX_Merge(src proto.Message)

func (*UndeployModelRequest) XXX_Size 

func (m *UndeployModelRequest) XXX_Size() int

func (*UndeployModelRequest) XXX_Unmarshal 

func (m *UndeployModelRequest) XXX_Unmarshal(b []byte) error

type UnimplementedAutoMlServer 

type UnimplementedAutoMlServer struct {
}

UnimplementedAutoMlServer can be embedded to have forward compatible implementations.

func (*UnimplementedAutoMlServer) CreateDataset 

func (*UnimplementedAutoMlServer) CreateDataset(ctx context.Context, req *CreateDatasetRequest) (*Dataset, error)

func (*UnimplementedAutoMlServer) CreateModel 

func (*UnimplementedAutoMlServer) CreateModel(ctx context.Context, req *CreateModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) DeleteDataset 

func (*UnimplementedAutoMlServer) DeleteDataset(ctx context.Context, req *DeleteDatasetRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) DeleteModel 

func (*UnimplementedAutoMlServer) DeleteModel(ctx context.Context, req *DeleteModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) DeployModel 

func (*UnimplementedAutoMlServer) DeployModel(ctx context.Context, req *DeployModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) ExportData 

func (*UnimplementedAutoMlServer) ExportData(ctx context.Context, req *ExportDataRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) ExportEvaluatedExamples 

func (*UnimplementedAutoMlServer) ExportEvaluatedExamples(ctx context.Context, req *ExportEvaluatedExamplesRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) ExportModel 

func (*UnimplementedAutoMlServer) ExportModel(ctx context.Context, req *ExportModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) GetAnnotationSpec 

func (*UnimplementedAutoMlServer) GetAnnotationSpec(ctx context.Context, req *GetAnnotationSpecRequest) (*AnnotationSpec, error)

func (*UnimplementedAutoMlServer) GetColumnSpec 

func (*UnimplementedAutoMlServer) GetColumnSpec(ctx context.Context, req *GetColumnSpecRequest) (*ColumnSpec, error)

func (*UnimplementedAutoMlServer) GetDataset 

func (*UnimplementedAutoMlServer) GetDataset(ctx context.Context, req *GetDatasetRequest) (*Dataset, error)

func (*UnimplementedAutoMlServer) GetModel 

func (*UnimplementedAutoMlServer) GetModel(ctx context.Context, req *GetModelRequest) (*Model, error)

func (*UnimplementedAutoMlServer) GetModelEvaluation 

func (*UnimplementedAutoMlServer) GetModelEvaluation(ctx context.Context, req *GetModelEvaluationRequest) (*ModelEvaluation, error)

func (*UnimplementedAutoMlServer) GetTableSpec 

func (*UnimplementedAutoMlServer) GetTableSpec(ctx context.Context, req *GetTableSpecRequest) (*TableSpec, error)

func (*UnimplementedAutoMlServer) ImportData 

func (*UnimplementedAutoMlServer) ImportData(ctx context.Context, req *ImportDataRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) ListColumnSpecs 

func (*UnimplementedAutoMlServer) ListColumnSpecs(ctx context.Context, req *ListColumnSpecsRequest) (*ListColumnSpecsResponse, error)

func (*UnimplementedAutoMlServer) ListDatasets 

func (*UnimplementedAutoMlServer) ListDatasets(ctx context.Context, req *ListDatasetsRequest) (*ListDatasetsResponse, error)

func (*UnimplementedAutoMlServer) ListModelEvaluations 

func (*UnimplementedAutoMlServer) ListModelEvaluations(ctx context.Context, req *ListModelEvaluationsRequest) (*ListModelEvaluationsResponse, error)

func (*UnimplementedAutoMlServer) ListModels 

func (*UnimplementedAutoMlServer) ListModels(ctx context.Context, req *ListModelsRequest) (*ListModelsResponse, error)

func (*UnimplementedAutoMlServer) ListTableSpecs 

func (*UnimplementedAutoMlServer) ListTableSpecs(ctx context.Context, req *ListTableSpecsRequest) (*ListTableSpecsResponse, error)

func (*UnimplementedAutoMlServer) UndeployModel 

func (*UnimplementedAutoMlServer) UndeployModel(ctx context.Context, req *UndeployModelRequest) (*longrunning.Operation, error)

func (*UnimplementedAutoMlServer) UpdateColumnSpec 

func (*UnimplementedAutoMlServer) UpdateColumnSpec(ctx context.Context, req *UpdateColumnSpecRequest) (*ColumnSpec, error)

func (*UnimplementedAutoMlServer) UpdateDataset 

func (*UnimplementedAutoMlServer) UpdateDataset(ctx context.Context, req *UpdateDatasetRequest) (*Dataset, error)

func (*UnimplementedAutoMlServer) UpdateTableSpec 

func (*UnimplementedAutoMlServer) UpdateTableSpec(ctx context.Context, req *UpdateTableSpecRequest) (*TableSpec, error)

type UnimplementedPredictionServiceServer 

type UnimplementedPredictionServiceServer struct {
}

UnimplementedPredictionServiceServer can be embedded to have forward compatible implementations.

func (*UnimplementedPredictionServiceServer) BatchPredict 

func (*UnimplementedPredictionServiceServer) BatchPredict(ctx context.Context, req *BatchPredictRequest) (*longrunning.Operation, error)

func (*UnimplementedPredictionServiceServer) Predict 

func (*UnimplementedPredictionServiceServer) Predict(ctx context.Context, req *PredictRequest) (*PredictResponse, error)

type UpdateColumnSpecRequest 

type UpdateColumnSpecRequest struct {
	// Required. The column spec which replaces the resource on the server.
	ColumnSpec *ColumnSpec `protobuf:"bytes,1,opt,name=column_spec,json=columnSpec,proto3" json:"column_spec,omitempty"`
	// The update mask applies to the resource.
	UpdateMask           *field_mask.FieldMask `protobuf:"bytes,2,opt,name=update_mask,json=updateMask,proto3" json:"update_mask,omitempty"`
	XXX_NoUnkeyedLiteral struct{}              `json:"-"`
	XXX_unrecognized     []byte                `json:"-"`
	XXX_sizecache        int32                 `json:"-"`
}

Request message for [AutoMl.UpdateColumnSpec][google.cloud.automl.v1beta1.AutoMl.UpdateColumnSpec]

func (*UpdateColumnSpecRequest) Descriptor 

func (*UpdateColumnSpecRequest) Descriptor() ([]byte, []int)

func (*UpdateColumnSpecRequest) GetColumnSpec 

func (m *UpdateColumnSpecRequest) GetColumnSpec() *ColumnSpec

func (*UpdateColumnSpecRequest) GetUpdateMask 

func (m *UpdateColumnSpecRequest) GetUpdateMask() *field_mask.FieldMask

func (*UpdateColumnSpecRequest) ProtoMessage 

func (*UpdateColumnSpecRequest) ProtoMessage()

func (*UpdateColumnSpecRequest) Reset 

func (m *UpdateColumnSpecRequest) Reset()

func (*UpdateColumnSpecRequest) String 

func (m *UpdateColumnSpecRequest) String() string

func (*UpdateColumnSpecRequest) XXX_DiscardUnknown 

func (m *UpdateColumnSpecRequest) XXX_DiscardUnknown()

func (*UpdateColumnSpecRequest) XXX_Marshal 

func (m *UpdateColumnSpecRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*UpdateColumnSpecRequest) XXX_Merge 

func (m *UpdateColumnSpecRequest) XXX_Merge(src proto.Message)

func (*UpdateColumnSpecRequest) XXX_Size 

func (m *UpdateColumnSpecRequest) XXX_Size() int

func (*UpdateColumnSpecRequest) XXX_Unmarshal 

func (m *UpdateColumnSpecRequest) XXX_Unmarshal(b []byte) error

type UpdateDatasetRequest 

type UpdateDatasetRequest struct {
	// Required. The dataset which replaces the resource on the server.
	Dataset *Dataset `protobuf:"bytes,1,opt,name=dataset,proto3" json:"dataset,omitempty"`
	// The update mask applies to the resource.
	UpdateMask           *field_mask.FieldMask `protobuf:"bytes,2,opt,name=update_mask,json=updateMask,proto3" json:"update_mask,omitempty"`
	XXX_NoUnkeyedLiteral struct{}              `json:"-"`
	XXX_unrecognized     []byte                `json:"-"`
	XXX_sizecache        int32                 `json:"-"`
}

Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1beta1.AutoMl.UpdateDataset]

func (*UpdateDatasetRequest) Descriptor 

func (*UpdateDatasetRequest) Descriptor() ([]byte, []int)

func (*UpdateDatasetRequest) GetDataset 

func (m *UpdateDatasetRequest) GetDataset() *Dataset

func (*UpdateDatasetRequest) GetUpdateMask 

func (m *UpdateDatasetRequest) GetUpdateMask() *field_mask.FieldMask

func (*UpdateDatasetRequest) ProtoMessage 

func (*UpdateDatasetRequest) ProtoMessage()

func (*UpdateDatasetRequest) Reset 

func (m *UpdateDatasetRequest) Reset()

func (*UpdateDatasetRequest) String 

func (m *UpdateDatasetRequest) String() string

func (*UpdateDatasetRequest) XXX_DiscardUnknown 

func (m *UpdateDatasetRequest) XXX_DiscardUnknown()

func (*UpdateDatasetRequest) XXX_Marshal 

func (m *UpdateDatasetRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*UpdateDatasetRequest) XXX_Merge 

func (m *UpdateDatasetRequest) XXX_Merge(src proto.Message)

func (*UpdateDatasetRequest) XXX_Size 

func (m *UpdateDatasetRequest) XXX_Size() int

func (*UpdateDatasetRequest) XXX_Unmarshal 

func (m *UpdateDatasetRequest) XXX_Unmarshal(b []byte) error

type UpdateTableSpecRequest 

type UpdateTableSpecRequest struct {
	// Required. The table spec which replaces the resource on the server.
	TableSpec *TableSpec `protobuf:"bytes,1,opt,name=table_spec,json=tableSpec,proto3" json:"table_spec,omitempty"`
	// The update mask applies to the resource.
	UpdateMask           *field_mask.FieldMask `protobuf:"bytes,2,opt,name=update_mask,json=updateMask,proto3" json:"update_mask,omitempty"`
	XXX_NoUnkeyedLiteral struct{}              `json:"-"`
	XXX_unrecognized     []byte                `json:"-"`
	XXX_sizecache        int32                 `json:"-"`
}

Request message for [AutoMl.UpdateTableSpec][google.cloud.automl.v1beta1.AutoMl.UpdateTableSpec]

func (*UpdateTableSpecRequest) Descriptor 

func (*UpdateTableSpecRequest) Descriptor() ([]byte, []int)

func (*UpdateTableSpecRequest) GetTableSpec 

func (m *UpdateTableSpecRequest) GetTableSpec() *TableSpec

func (*UpdateTableSpecRequest) GetUpdateMask 

func (m *UpdateTableSpecRequest) GetUpdateMask() *field_mask.FieldMask

func (*UpdateTableSpecRequest) ProtoMessage 

func (*UpdateTableSpecRequest) ProtoMessage()

func (*UpdateTableSpecRequest) Reset 

func (m *UpdateTableSpecRequest) Reset()

func (*UpdateTableSpecRequest) String 

func (m *UpdateTableSpecRequest) String() string

func (*UpdateTableSpecRequest) XXX_DiscardUnknown 

func (m *UpdateTableSpecRequest) XXX_DiscardUnknown()

func (*UpdateTableSpecRequest) XXX_Marshal 

func (m *UpdateTableSpecRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*UpdateTableSpecRequest) XXX_Merge 

func (m *UpdateTableSpecRequest) XXX_Merge(src proto.Message)

func (*UpdateTableSpecRequest) XXX_Size 

func (m *UpdateTableSpecRequest) XXX_Size() int

func (*UpdateTableSpecRequest) XXX_Unmarshal 

func (m *UpdateTableSpecRequest) XXX_Unmarshal(b []byte) error

type VideoClassificationAnnotation 

type VideoClassificationAnnotation struct {
	// Output only. Expresses the type of video classification. Possible values:
	//
	// *  `segment` - Classification done on a specified by user
	//        time segment of a video. AnnotationSpec is answered to be present
	//        in that time segment, if it is present in any part of it. The video
	//        ML model evaluations are done only for this type of classification.
	//
	// *  `shot`- Shot-level classification.
	//        AutoML Video Intelligence determines the boundaries
	//        for each camera shot in the entire segment of the video that user
	//        specified in the request configuration. AutoML Video Intelligence
	//        then returns labels and their confidence scores for each detected
	//        shot, along with the start and end time of the shot.
	//        WARNING: Model evaluation is not done for this classification type,
	//        the quality of it depends on training data, but there are no
	//        metrics provided to describe that quality.
	//
	// *  `1s_interval` - AutoML Video Intelligence returns labels and their
	//        confidence scores for each second of the entire segment of the video
	//        that user specified in the request configuration.
	//        WARNING: Model evaluation is not done for this classification type,
	//        the quality of it depends on training data, but there are no
	//        metrics provided to describe that quality.
	Type string `protobuf:"bytes,1,opt,name=type,proto3" json:"type,omitempty"`
	// Output only . The classification details of this annotation.
	ClassificationAnnotation *ClassificationAnnotation `` /* 133-byte string literal not displayed */
	// Output only . The time segment of the video to which the
	// annotation applies.
	TimeSegment          *TimeSegment `protobuf:"bytes,3,opt,name=time_segment,json=timeSegment,proto3" json:"time_segment,omitempty"`
	XXX_NoUnkeyedLiteral struct{}     `json:"-"`
	XXX_unrecognized     []byte       `json:"-"`
	XXX_sizecache        int32        `json:"-"`
}

Contains annotation details specific to video classification.

func (*VideoClassificationAnnotation) Descriptor 

func (*VideoClassificationAnnotation) Descriptor() ([]byte, []int)

func (*VideoClassificationAnnotation) GetClassificationAnnotation 

func (m *VideoClassificationAnnotation) GetClassificationAnnotation() *ClassificationAnnotation

func (*VideoClassificationAnnotation) GetTimeSegment 

func (m *VideoClassificationAnnotation) GetTimeSegment() *TimeSegment

func (*VideoClassificationAnnotation) GetType 

func (m *VideoClassificationAnnotation) GetType() string

func (*VideoClassificationAnnotation) ProtoMessage 

func (*VideoClassificationAnnotation) ProtoMessage()

func (*VideoClassificationAnnotation) Reset 

func (m *VideoClassificationAnnotation) Reset()

func (*VideoClassificationAnnotation) String 

func (m *VideoClassificationAnnotation) String() string

func (*VideoClassificationAnnotation) XXX_DiscardUnknown 

func (m *VideoClassificationAnnotation) XXX_DiscardUnknown()

func (*VideoClassificationAnnotation) XXX_Marshal 

func (m *VideoClassificationAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*VideoClassificationAnnotation) XXX_Merge 

func (m *VideoClassificationAnnotation) XXX_Merge(src proto.Message)

func (*VideoClassificationAnnotation) XXX_Size 

func (m *VideoClassificationAnnotation) XXX_Size() int

func (*VideoClassificationAnnotation) XXX_Unmarshal 

func (m *VideoClassificationAnnotation) XXX_Unmarshal(b []byte) error

type VideoClassificationDatasetMetadata 

type VideoClassificationDatasetMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Dataset metadata specific to video classification. All Video Classification datasets are treated as multi label.

func (*VideoClassificationDatasetMetadata) Descriptor 

func (*VideoClassificationDatasetMetadata) Descriptor() ([]byte, []int)

func (*VideoClassificationDatasetMetadata) ProtoMessage 

func (*VideoClassificationDatasetMetadata) ProtoMessage()

func (*VideoClassificationDatasetMetadata) Reset 

func (m *VideoClassificationDatasetMetadata) Reset()

func (*VideoClassificationDatasetMetadata) String 

func (m *VideoClassificationDatasetMetadata) String() string

func (*VideoClassificationDatasetMetadata) XXX_DiscardUnknown 

func (m *VideoClassificationDatasetMetadata) XXX_DiscardUnknown()

func (*VideoClassificationDatasetMetadata) XXX_Marshal 

func (m *VideoClassificationDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*VideoClassificationDatasetMetadata) XXX_Merge 

func (m *VideoClassificationDatasetMetadata) XXX_Merge(src proto.Message)

func (*VideoClassificationDatasetMetadata) XXX_Size 

func (m *VideoClassificationDatasetMetadata) XXX_Size() int

func (*VideoClassificationDatasetMetadata) XXX_Unmarshal 

func (m *VideoClassificationDatasetMetadata) XXX_Unmarshal(b []byte) error

type VideoClassificationModelMetadata 

type VideoClassificationModelMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model metadata specific to video classification.

func (*VideoClassificationModelMetadata) Descriptor 

func (*VideoClassificationModelMetadata) Descriptor() ([]byte, []int)

func (*VideoClassificationModelMetadata) ProtoMessage 

func (*VideoClassificationModelMetadata) ProtoMessage()

func (*VideoClassificationModelMetadata) Reset 

func (m *VideoClassificationModelMetadata) Reset()

func (*VideoClassificationModelMetadata) String 

func (m *VideoClassificationModelMetadata) String() string

func (*VideoClassificationModelMetadata) XXX_DiscardUnknown 

func (m *VideoClassificationModelMetadata) XXX_DiscardUnknown()

func (*VideoClassificationModelMetadata) XXX_Marshal 

func (m *VideoClassificationModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*VideoClassificationModelMetadata) XXX_Merge 

func (m *VideoClassificationModelMetadata) XXX_Merge(src proto.Message)

func (*VideoClassificationModelMetadata) XXX_Size 

func (m *VideoClassificationModelMetadata) XXX_Size() int

func (*VideoClassificationModelMetadata) XXX_Unmarshal 

func (m *VideoClassificationModelMetadata) XXX_Unmarshal(b []byte) error

type VideoObjectTrackingAnnotation 

type VideoObjectTrackingAnnotation struct {
	// Optional. The instance of the object, expressed as a positive integer. Used to tell
	// apart objects of the same type (i.e. AnnotationSpec) when multiple are
	// present on a single example.
	// NOTE: Instance ID prediction quality is not a part of model evaluation and
	// is done as best effort. Especially in cases when an entity goes
	// off-screen for a longer time (minutes), when it comes back it may be given
	// a new instance ID.
	InstanceId string `protobuf:"bytes,1,opt,name=instance_id,json=instanceId,proto3" json:"instance_id,omitempty"`
	// Required. A time (frame) of a video to which this annotation pertains.
	// Represented as the duration since the video's start.
	TimeOffset *duration.Duration `protobuf:"bytes,2,opt,name=time_offset,json=timeOffset,proto3" json:"time_offset,omitempty"`
	// Required. The rectangle representing the object location on the frame (i.e.
	// at the time_offset of the video).
	BoundingBox *BoundingPoly `protobuf:"bytes,3,opt,name=bounding_box,json=boundingBox,proto3" json:"bounding_box,omitempty"`
	// Output only. The confidence that this annotation is positive for the video at
	// the time_offset, value in [0, 1], higher means higher positivity
	// confidence. For annotations created by the user the score is 1. When
	// user approves an annotation, the original float score is kept (and not
	// changed to 1).
	Score                float32  `protobuf:"fixed32,4,opt,name=score,proto3" json:"score,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Annotation details for video object tracking.

func (*VideoObjectTrackingAnnotation) Descriptor 

func (*VideoObjectTrackingAnnotation) Descriptor() ([]byte, []int)

func (*VideoObjectTrackingAnnotation) GetBoundingBox 

func (m *VideoObjectTrackingAnnotation) GetBoundingBox() *BoundingPoly

func (*VideoObjectTrackingAnnotation) GetInstanceId 

func (m *VideoObjectTrackingAnnotation) GetInstanceId() string

func (*VideoObjectTrackingAnnotation) GetScore 

func (m *VideoObjectTrackingAnnotation) GetScore() float32

func (*VideoObjectTrackingAnnotation) GetTimeOffset 

func (m *VideoObjectTrackingAnnotation) GetTimeOffset() *duration.Duration

func (*VideoObjectTrackingAnnotation) ProtoMessage 

func (*VideoObjectTrackingAnnotation) ProtoMessage()

func (*VideoObjectTrackingAnnotation) Reset 

func (m *VideoObjectTrackingAnnotation) Reset()

func (*VideoObjectTrackingAnnotation) String 

func (m *VideoObjectTrackingAnnotation) String() string

func (*VideoObjectTrackingAnnotation) XXX_DiscardUnknown 

func (m *VideoObjectTrackingAnnotation) XXX_DiscardUnknown()

func (*VideoObjectTrackingAnnotation) XXX_Marshal 

func (m *VideoObjectTrackingAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*VideoObjectTrackingAnnotation) XXX_Merge 

func (m *VideoObjectTrackingAnnotation) XXX_Merge(src proto.Message)

func (*VideoObjectTrackingAnnotation) XXX_Size 

func (m *VideoObjectTrackingAnnotation) XXX_Size() int

func (*VideoObjectTrackingAnnotation) XXX_Unmarshal 

func (m *VideoObjectTrackingAnnotation) XXX_Unmarshal(b []byte) error

type VideoObjectTrackingDatasetMetadata 

type VideoObjectTrackingDatasetMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Dataset metadata specific to video object tracking.

func (*VideoObjectTrackingDatasetMetadata) Descriptor 

func (*VideoObjectTrackingDatasetMetadata) Descriptor() ([]byte, []int)

func (*VideoObjectTrackingDatasetMetadata) ProtoMessage 

func (*VideoObjectTrackingDatasetMetadata) ProtoMessage()

func (*VideoObjectTrackingDatasetMetadata) Reset 

func (m *VideoObjectTrackingDatasetMetadata) Reset()

func (*VideoObjectTrackingDatasetMetadata) String 

func (m *VideoObjectTrackingDatasetMetadata) String() string

func (*VideoObjectTrackingDatasetMetadata) XXX_DiscardUnknown 

func (m *VideoObjectTrackingDatasetMetadata) XXX_DiscardUnknown()

func (*VideoObjectTrackingDatasetMetadata) XXX_Marshal 

func (m *VideoObjectTrackingDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*VideoObjectTrackingDatasetMetadata) XXX_Merge 

func (m *VideoObjectTrackingDatasetMetadata) XXX_Merge(src proto.Message)

func (*VideoObjectTrackingDatasetMetadata) XXX_Size 

func (m *VideoObjectTrackingDatasetMetadata) XXX_Size() int

func (*VideoObjectTrackingDatasetMetadata) XXX_Unmarshal 

func (m *VideoObjectTrackingDatasetMetadata) XXX_Unmarshal(b []byte) error

type VideoObjectTrackingEvaluationMetrics 

type VideoObjectTrackingEvaluationMetrics struct {
	// Output only. The number of video frames used to create this evaluation.
	EvaluatedFrameCount int32 `protobuf:"varint,1,opt,name=evaluated_frame_count,json=evaluatedFrameCount,proto3" json:"evaluated_frame_count,omitempty"`
	// Output only. The total number of bounding boxes (i.e. summed over all
	// frames) the ground truth used to create this evaluation had.
	EvaluatedBoundingBoxCount int32 `` /* 141-byte string literal not displayed */
	// Output only. The bounding boxes match metrics for each
	// Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
	// and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
	// pair.
	BoundingBoxMetricsEntries []*BoundingBoxMetricsEntry `` /* 140-byte string literal not displayed */
	// Output only. The single metric for bounding boxes evaluation:
	// the mean_average_precision averaged over all bounding_box_metrics_entries.
	BoundingBoxMeanAveragePrecision float32  `` /* 162-byte string literal not displayed */
	XXX_NoUnkeyedLiteral            struct{} `json:"-"`
	XXX_unrecognized                []byte   `json:"-"`
	XXX_sizecache                   int32    `json:"-"`
}

Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID).

func (*VideoObjectTrackingEvaluationMetrics) Descriptor 

func (*VideoObjectTrackingEvaluationMetrics) Descriptor() ([]byte, []int)

func (*VideoObjectTrackingEvaluationMetrics) GetBoundingBoxMeanAveragePrecision 

func (m *VideoObjectTrackingEvaluationMetrics) GetBoundingBoxMeanAveragePrecision() float32

func (*VideoObjectTrackingEvaluationMetrics) GetBoundingBoxMetricsEntries 

func (m *VideoObjectTrackingEvaluationMetrics) GetBoundingBoxMetricsEntries() []*BoundingBoxMetricsEntry

func (*VideoObjectTrackingEvaluationMetrics) GetEvaluatedBoundingBoxCount 

func (m *VideoObjectTrackingEvaluationMetrics) GetEvaluatedBoundingBoxCount() int32

func (*VideoObjectTrackingEvaluationMetrics) GetEvaluatedFrameCount 

func (m *VideoObjectTrackingEvaluationMetrics) GetEvaluatedFrameCount() int32

func (*VideoObjectTrackingEvaluationMetrics) ProtoMessage 

func (*VideoObjectTrackingEvaluationMetrics) ProtoMessage()

func (*VideoObjectTrackingEvaluationMetrics) Reset 

func (m *VideoObjectTrackingEvaluationMetrics) Reset()

func (*VideoObjectTrackingEvaluationMetrics) String 

func (m *VideoObjectTrackingEvaluationMetrics) String() string

func (*VideoObjectTrackingEvaluationMetrics) XXX_DiscardUnknown 

func (m *VideoObjectTrackingEvaluationMetrics) XXX_DiscardUnknown()

func (*VideoObjectTrackingEvaluationMetrics) XXX_Marshal 

func (m *VideoObjectTrackingEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*VideoObjectTrackingEvaluationMetrics) XXX_Merge 

func (m *VideoObjectTrackingEvaluationMetrics) XXX_Merge(src proto.Message)

func (*VideoObjectTrackingEvaluationMetrics) XXX_Size 

func (m *VideoObjectTrackingEvaluationMetrics) XXX_Size() int

func (*VideoObjectTrackingEvaluationMetrics) XXX_Unmarshal 

func (m *VideoObjectTrackingEvaluationMetrics) XXX_Unmarshal(b []byte) error

type VideoObjectTrackingModelMetadata 

type VideoObjectTrackingModelMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

Model metadata specific to video object tracking.

func (*VideoObjectTrackingModelMetadata) Descriptor 

func (*VideoObjectTrackingModelMetadata) Descriptor() ([]byte, []int)

func (*VideoObjectTrackingModelMetadata) ProtoMessage 

func (*VideoObjectTrackingModelMetadata) ProtoMessage()

func (*VideoObjectTrackingModelMetadata) Reset 

func (m *VideoObjectTrackingModelMetadata) Reset()

func (*VideoObjectTrackingModelMetadata) String 

func (m *VideoObjectTrackingModelMetadata) String() string

func (*VideoObjectTrackingModelMetadata) XXX_DiscardUnknown 

func (m *VideoObjectTrackingModelMetadata) XXX_DiscardUnknown()

func (*VideoObjectTrackingModelMetadata) XXX_Marshal 

func (m *VideoObjectTrackingModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error)

func (*VideoObjectTrackingModelMetadata) XXX_Merge 

func (m *VideoObjectTrackingModelMetadata) XXX_Merge(src proto.Message)

func (*VideoObjectTrackingModelMetadata) XXX_Size 

func (m *VideoObjectTrackingModelMetadata) XXX_Size() int

func (*VideoObjectTrackingModelMetadata) XXX_Unmarshal 

func (m *VideoObjectTrackingModelMetadata) XXX_Unmarshal(b []byte) error

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