Variables
ClassificationType_name, ClassificationType_value
var (
ClassificationType_name = map[int32]string{
0: "CLASSIFICATION_TYPE_UNSPECIFIED",
1: "MULTICLASS",
2: "MULTILABEL",
}
ClassificationType_value = map[string]int32{
"CLASSIFICATION_TYPE_UNSPECIFIED": 0,
"MULTICLASS": 1,
"MULTILABEL": 2,
}
)Enum value maps for ClassificationType.
DocumentDimensions_DocumentDimensionUnit_name, DocumentDimensions_DocumentDimensionUnit_value
var (
DocumentDimensions_DocumentDimensionUnit_name = map[int32]string{
0: "DOCUMENT_DIMENSION_UNIT_UNSPECIFIED",
1: "INCH",
2: "CENTIMETER",
3: "POINT",
}
DocumentDimensions_DocumentDimensionUnit_value = map[string]int32{
"DOCUMENT_DIMENSION_UNIT_UNSPECIFIED": 0,
"INCH": 1,
"CENTIMETER": 2,
"POINT": 3,
}
)Enum value maps for DocumentDimensions_DocumentDimensionUnit.
Document_Layout_TextSegmentType_name, Document_Layout_TextSegmentType_value
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",
}
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,
}
)Enum value maps for Document_Layout_TextSegmentType.
TypeCode_name, TypeCode_value
var (
TypeCode_name = map[int32]string{
0: "TYPE_CODE_UNSPECIFIED",
3: "FLOAT64",
4: "TIMESTAMP",
6: "STRING",
8: "ARRAY",
9: "STRUCT",
10: "CATEGORY",
}
TypeCode_value = map[string]int32{
"TYPE_CODE_UNSPECIFIED": 0,
"FLOAT64": 3,
"TIMESTAMP": 4,
"STRING": 6,
"ARRAY": 8,
"STRUCT": 9,
"CATEGORY": 10,
}
)Enum value maps for TypeCode.
Model_DeploymentState_name, Model_DeploymentState_value
var (
Model_DeploymentState_name = map[int32]string{
0: "DEPLOYMENT_STATE_UNSPECIFIED",
1: "DEPLOYED",
2: "UNDEPLOYED",
}
Model_DeploymentState_value = map[string]int32{
"DEPLOYMENT_STATE_UNSPECIFIED": 0,
"DEPLOYED": 1,
"UNDEPLOYED": 2,
}
)Enum value maps for Model_DeploymentState.
File_google_cloud_automl_v1beta1_annotation_payload_proto
var File_google_cloud_automl_v1beta1_annotation_payload_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_annotation_spec_proto
var File_google_cloud_automl_v1beta1_annotation_spec_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_classification_proto
var File_google_cloud_automl_v1beta1_classification_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_column_spec_proto
var File_google_cloud_automl_v1beta1_column_spec_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_data_items_proto
var File_google_cloud_automl_v1beta1_data_items_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_data_stats_proto
var File_google_cloud_automl_v1beta1_data_stats_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_data_types_proto
var File_google_cloud_automl_v1beta1_data_types_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_dataset_proto
var File_google_cloud_automl_v1beta1_dataset_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_detection_proto
var File_google_cloud_automl_v1beta1_detection_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_geometry_proto
var File_google_cloud_automl_v1beta1_geometry_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_image_proto
var File_google_cloud_automl_v1beta1_image_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_io_proto
var File_google_cloud_automl_v1beta1_io_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_model_evaluation_proto
var File_google_cloud_automl_v1beta1_model_evaluation_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_model_proto
var File_google_cloud_automl_v1beta1_model_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_operations_proto
var File_google_cloud_automl_v1beta1_operations_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_prediction_service_proto
var File_google_cloud_automl_v1beta1_prediction_service_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_ranges_proto
var File_google_cloud_automl_v1beta1_ranges_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_regression_proto
var File_google_cloud_automl_v1beta1_regression_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_service_proto
var File_google_cloud_automl_v1beta1_service_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_table_spec_proto
var File_google_cloud_automl_v1beta1_table_spec_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_tables_proto
var File_google_cloud_automl_v1beta1_tables_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_temporal_proto
var File_google_cloud_automl_v1beta1_temporal_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_text_extraction_proto
var File_google_cloud_automl_v1beta1_text_extraction_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_text_proto
var File_google_cloud_automl_v1beta1_text_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_text_segment_proto
var File_google_cloud_automl_v1beta1_text_segment_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_text_sentiment_proto
var File_google_cloud_automl_v1beta1_text_sentiment_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_translation_proto
var File_google_cloud_automl_v1beta1_translation_proto protoreflect.FileDescriptorFile_google_cloud_automl_v1beta1_video_proto
var File_google_cloud_automl_v1beta1_video_proto protoreflect.FileDescriptorFunctions
func RegisterAutoMlServer
func RegisterAutoMlServer(s *grpc.Server, srv AutoMlServer)func RegisterPredictionServiceServer
func RegisterPredictionServiceServer(s *grpc.Server, srv PredictionServiceServer)AnnotationPayload
type AnnotationPayload struct {
// Output only . Additional information about the annotation
// specific to the AutoML domain.
//
// Types that are assignable 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"`
// contains filtered or unexported fields
}Contains annotation information that is relevant to AutoML.
func (*AnnotationPayload) Descriptor
func (*AnnotationPayload) Descriptor() ([]byte, []int)Deprecated: Use AnnotationPayload.ProtoReflect.Descriptor instead.
func (*AnnotationPayload) GetAnnotationSpecId
func (x *AnnotationPayload) GetAnnotationSpecId() stringfunc (*AnnotationPayload) GetClassification
func (x *AnnotationPayload) GetClassification() *ClassificationAnnotationfunc (*AnnotationPayload) GetDetail
func (m *AnnotationPayload) GetDetail() isAnnotationPayload_Detailfunc (*AnnotationPayload) GetDisplayName
func (x *AnnotationPayload) GetDisplayName() stringfunc (*AnnotationPayload) GetImageObjectDetection
func (x *AnnotationPayload) GetImageObjectDetection() *ImageObjectDetectionAnnotationfunc (*AnnotationPayload) GetTables
func (x *AnnotationPayload) GetTables() *TablesAnnotationfunc (*AnnotationPayload) GetTextExtraction
func (x *AnnotationPayload) GetTextExtraction() *TextExtractionAnnotationfunc (*AnnotationPayload) GetTextSentiment
func (x *AnnotationPayload) GetTextSentiment() *TextSentimentAnnotationfunc (*AnnotationPayload) GetTranslation
func (x *AnnotationPayload) GetTranslation() *TranslationAnnotationfunc (*AnnotationPayload) GetVideoClassification
func (x *AnnotationPayload) GetVideoClassification() *VideoClassificationAnnotationfunc (*AnnotationPayload) GetVideoObjectTracking
func (x *AnnotationPayload) GetVideoObjectTracking() *VideoObjectTrackingAnnotationfunc (*AnnotationPayload) ProtoMessage
func (*AnnotationPayload) ProtoMessage()func (*AnnotationPayload) ProtoReflect
func (x *AnnotationPayload) ProtoReflect() protoreflect.Messagefunc (*AnnotationPayload) Reset
func (x *AnnotationPayload) Reset()func (*AnnotationPayload) String
func (x *AnnotationPayload) String() stringAnnotationPayload_Classification
type AnnotationPayload_Classification struct {
// Annotation details for content or image classification.
Classification *ClassificationAnnotation `protobuf:"bytes,3,opt,name=classification,proto3,oneof"`
}AnnotationPayload_ImageObjectDetection
type AnnotationPayload_ImageObjectDetection struct {
// Annotation details for image object detection.
ImageObjectDetection *ImageObjectDetectionAnnotation `protobuf:"bytes,4,opt,name=image_object_detection,json=imageObjectDetection,proto3,oneof"`
}AnnotationPayload_Tables
type AnnotationPayload_Tables struct {
// Annotation details for Tables.
Tables *TablesAnnotation `protobuf:"bytes,10,opt,name=tables,proto3,oneof"`
}AnnotationPayload_TextExtraction
type AnnotationPayload_TextExtraction struct {
// Annotation details for text extraction.
TextExtraction *TextExtractionAnnotation `protobuf:"bytes,6,opt,name=text_extraction,json=textExtraction,proto3,oneof"`
}AnnotationPayload_TextSentiment
type AnnotationPayload_TextSentiment struct {
// Annotation details for text sentiment.
TextSentiment *TextSentimentAnnotation `protobuf:"bytes,7,opt,name=text_sentiment,json=textSentiment,proto3,oneof"`
}AnnotationPayload_Translation
type AnnotationPayload_Translation struct {
// Annotation details for translation.
Translation *TranslationAnnotation `protobuf:"bytes,2,opt,name=translation,proto3,oneof"`
}AnnotationPayload_VideoClassification
type AnnotationPayload_VideoClassification struct {
// Annotation details for video classification.
// Returned for Video Classification predictions.
VideoClassification *VideoClassificationAnnotation `protobuf:"bytes,9,opt,name=video_classification,json=videoClassification,proto3,oneof"`
}AnnotationPayload_VideoObjectTracking
type AnnotationPayload_VideoObjectTracking struct {
// Annotation details for video object tracking.
VideoObjectTracking *VideoObjectTrackingAnnotation `protobuf:"bytes,8,opt,name=video_object_tracking,json=videoObjectTracking,proto3,oneof"`
}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"`
// contains filtered or unexported fields
}A definition of an annotation spec.
func (*AnnotationSpec) Descriptor
func (*AnnotationSpec) Descriptor() ([]byte, []int)Deprecated: Use AnnotationSpec.ProtoReflect.Descriptor instead.
func (*AnnotationSpec) GetDisplayName
func (x *AnnotationSpec) GetDisplayName() stringfunc (*AnnotationSpec) GetExampleCount
func (x *AnnotationSpec) GetExampleCount() int32func (*AnnotationSpec) GetName
func (x *AnnotationSpec) GetName() stringfunc (*AnnotationSpec) ProtoMessage
func (*AnnotationSpec) ProtoMessage()func (*AnnotationSpec) ProtoReflect
func (x *AnnotationSpec) ProtoReflect() protoreflect.Messagefunc (*AnnotationSpec) Reset
func (x *AnnotationSpec) Reset()func (*AnnotationSpec) String
func (x *AnnotationSpec) String() stringArrayStats
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"`
// contains filtered or unexported fields
}The data statistics of a series of ARRAY values.
func (*ArrayStats) Descriptor
func (*ArrayStats) Descriptor() ([]byte, []int)Deprecated: Use ArrayStats.ProtoReflect.Descriptor instead.
func (*ArrayStats) GetMemberStats
func (x *ArrayStats) GetMemberStats() *DataStatsfunc (*ArrayStats) ProtoMessage
func (*ArrayStats) ProtoMessage()func (*ArrayStats) ProtoReflect
func (x *ArrayStats) ProtoReflect() protoreflect.Messagefunc (*ArrayStats) Reset
func (x *ArrayStats) Reset()func (*ArrayStats) String
func (x *ArrayStats) String() stringAutoMlClient
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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) AutoMlClientAutoMlServer
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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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) (*longrunningpb.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.
BatchPredictInputConfig
type BatchPredictInputConfig struct {
// Required. The source of the input.
//
// Types that are assignable to Source:
// *BatchPredictInputConfig_GcsSource
// *BatchPredictInputConfig_BigquerySource
Source isBatchPredictInputConfig_Source `protobuf_oneof:"source"`
// contains filtered or unexported fields
}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)Deprecated: Use BatchPredictInputConfig.ProtoReflect.Descriptor instead.
func (*BatchPredictInputConfig) GetBigquerySource
func (x *BatchPredictInputConfig) GetBigquerySource() *BigQuerySourcefunc (*BatchPredictInputConfig) GetGcsSource
func (x *BatchPredictInputConfig) GetGcsSource() *GcsSourcefunc (*BatchPredictInputConfig) GetSource
func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Sourcefunc (*BatchPredictInputConfig) ProtoMessage
func (*BatchPredictInputConfig) ProtoMessage()func (*BatchPredictInputConfig) ProtoReflect
func (x *BatchPredictInputConfig) ProtoReflect() protoreflect.Messagefunc (*BatchPredictInputConfig) Reset
func (x *BatchPredictInputConfig) Reset()func (*BatchPredictInputConfig) String
func (x *BatchPredictInputConfig) String() stringBatchPredictInputConfig_BigquerySource
type BatchPredictInputConfig_BigquerySource struct {
// The BigQuery location for the input content.
BigquerySource *BigQuerySource `protobuf:"bytes,2,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}BatchPredictInputConfig_GcsSource
type BatchPredictInputConfig_GcsSource struct {
// The Google Cloud Storage location for the input content.
GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}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"`
// contains filtered or unexported fields
}Details of BatchPredict operation.
func (*BatchPredictOperationMetadata) Descriptor
func (*BatchPredictOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use BatchPredictOperationMetadata.ProtoReflect.Descriptor instead.
func (*BatchPredictOperationMetadata) GetInputConfig
func (x *BatchPredictOperationMetadata) GetInputConfig() *BatchPredictInputConfigfunc (*BatchPredictOperationMetadata) GetOutputInfo
func (x *BatchPredictOperationMetadata) GetOutputInfo() *BatchPredictOperationMetadata_BatchPredictOutputInfofunc (*BatchPredictOperationMetadata) ProtoMessage
func (*BatchPredictOperationMetadata) ProtoMessage()func (*BatchPredictOperationMetadata) ProtoReflect
func (x *BatchPredictOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*BatchPredictOperationMetadata) Reset
func (x *BatchPredictOperationMetadata) Reset()func (*BatchPredictOperationMetadata) String
func (x *BatchPredictOperationMetadata) String() stringBatchPredictOperationMetadata_BatchPredictOutputInfo
type BatchPredictOperationMetadata_BatchPredictOutputInfo struct {
// The output location into which prediction output is written.
//
// Types that are assignable to OutputLocation:
// *BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory
// *BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset
OutputLocation isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation `protobuf_oneof:"output_location"`
// contains filtered or unexported fields
}Further describes this batch predict's output. Supplements
[BatchPredictOutputConfig][google.cloud.automl.v1beta1.BatchPredictOutputConfig].
func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Descriptor
func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Descriptor() ([]byte, []int)Deprecated: Use BatchPredictOperationMetadata_BatchPredictOutputInfo.ProtoReflect.Descriptor instead.
func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetBigqueryOutputDataset
func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetBigqueryOutputDataset() stringfunc (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory
func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory() stringfunc (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation
func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation() isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocationfunc (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage
func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage()func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect
func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect() protoreflect.Messagefunc (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset
func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset()func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) String
func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) String() stringBatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset
type BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset struct {
// The path of the BigQuery dataset created, in bq://projectId.bqDatasetId
// format, into which the prediction output is written.
BigqueryOutputDataset string `protobuf:"bytes,2,opt,name=bigquery_output_dataset,json=bigqueryOutputDataset,proto3,oneof"`
}BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory
type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory struct {
// The full path of the Google Cloud Storage directory created, into which
// the prediction output is written.
GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3,oneof"`
}BatchPredictOutputConfig
type BatchPredictOutputConfig struct {
// Required. The destination of the output.
//
// Types that are assignable to Destination:
// *BatchPredictOutputConfig_GcsDestination
// *BatchPredictOutputConfig_BigqueryDestination
Destination isBatchPredictOutputConfig_Destination `protobuf_oneof:"destination"`
// contains filtered or unexported fields
}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-
- For Image Classification:
In the created directory files
image_classification_1.jsonl,image_classification_2.jsonl,...,image_classification_N.jsonlwill 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 additionalerrors_1.jsonl,errors_2.jsonl,...,errors_N.jsonlfiles 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)Deprecated: Use BatchPredictOutputConfig.ProtoReflect.Descriptor instead.
func (*BatchPredictOutputConfig) GetBigqueryDestination
func (x *BatchPredictOutputConfig) GetBigqueryDestination() *BigQueryDestinationfunc (*BatchPredictOutputConfig) GetDestination
func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destinationfunc (*BatchPredictOutputConfig) GetGcsDestination
func (x *BatchPredictOutputConfig) GetGcsDestination() *GcsDestinationfunc (*BatchPredictOutputConfig) ProtoMessage
func (*BatchPredictOutputConfig) ProtoMessage()func (*BatchPredictOutputConfig) ProtoReflect
func (x *BatchPredictOutputConfig) ProtoReflect() protoreflect.Messagefunc (*BatchPredictOutputConfig) Reset
func (x *BatchPredictOutputConfig) Reset()func (*BatchPredictOutputConfig) String
func (x *BatchPredictOutputConfig) String() stringBatchPredictOutputConfig_BigqueryDestination
type BatchPredictOutputConfig_BigqueryDestination struct {
// The BigQuery location where the output is to be written to.
BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}BatchPredictOutputConfig_GcsDestination
type BatchPredictOutputConfig_GcsDestination struct {
// The Google Cloud Storage location of the directory where the output is to
// be written to.
GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}BatchPredictRequest
type BatchPredictRequest struct {
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
InputConfig *BatchPredictInputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
OutputConfig *BatchPredictOutputConfig `protobuf:"bytes,4,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
Params map[string]string "" /* 153 byte string literal not displayed */
}Request message for [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict].
func (*BatchPredictRequest) Descriptor
func (*BatchPredictRequest) Descriptor() ([]byte, []int)Deprecated: Use BatchPredictRequest.ProtoReflect.Descriptor instead.
func (*BatchPredictRequest) GetInputConfig
func (x *BatchPredictRequest) GetInputConfig() *BatchPredictInputConfigfunc (*BatchPredictRequest) GetName
func (x *BatchPredictRequest) GetName() stringfunc (*BatchPredictRequest) GetOutputConfig
func (x *BatchPredictRequest) GetOutputConfig() *BatchPredictOutputConfigfunc (*BatchPredictRequest) GetParams
func (x *BatchPredictRequest) GetParams() map[string]stringfunc (*BatchPredictRequest) ProtoMessage
func (*BatchPredictRequest) ProtoMessage()func (*BatchPredictRequest) ProtoReflect
func (x *BatchPredictRequest) ProtoReflect() protoreflect.Messagefunc (*BatchPredictRequest) Reset
func (x *BatchPredictRequest) Reset()func (*BatchPredictRequest) String
func (x *BatchPredictRequest) String() stringBatchPredictResult
type BatchPredictResult struct {
Metadata map[string]string "" /* 157 byte string literal not displayed */
}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)Deprecated: Use BatchPredictResult.ProtoReflect.Descriptor instead.
func (*BatchPredictResult) GetMetadata
func (x *BatchPredictResult) GetMetadata() map[string]stringfunc (*BatchPredictResult) ProtoMessage
func (*BatchPredictResult) ProtoMessage()func (*BatchPredictResult) ProtoReflect
func (x *BatchPredictResult) ProtoReflect() protoreflect.Messagefunc (*BatchPredictResult) Reset
func (x *BatchPredictResult) Reset()func (*BatchPredictResult) String
func (x *BatchPredictResult) String() stringBigQueryDestination
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"`
// contains filtered or unexported fields
}The BigQuery location for the output content.
func (*BigQueryDestination) Descriptor
func (*BigQueryDestination) Descriptor() ([]byte, []int)Deprecated: Use BigQueryDestination.ProtoReflect.Descriptor instead.
func (*BigQueryDestination) GetOutputUri
func (x *BigQueryDestination) GetOutputUri() stringfunc (*BigQueryDestination) ProtoMessage
func (*BigQueryDestination) ProtoMessage()func (*BigQueryDestination) ProtoReflect
func (x *BigQueryDestination) ProtoReflect() protoreflect.Messagefunc (*BigQueryDestination) Reset
func (x *BigQueryDestination) Reset()func (*BigQueryDestination) String
func (x *BigQueryDestination) String() stringBigQuerySource
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"`
// contains filtered or unexported fields
}The BigQuery location for the input content.
func (*BigQuerySource) Descriptor
func (*BigQuerySource) Descriptor() ([]byte, []int)Deprecated: Use BigQuerySource.ProtoReflect.Descriptor instead.
func (*BigQuerySource) GetInputUri
func (x *BigQuerySource) GetInputUri() stringfunc (*BigQuerySource) ProtoMessage
func (*BigQuerySource) ProtoMessage()func (*BigQuerySource) ProtoReflect
func (x *BigQuerySource) ProtoReflect() protoreflect.Messagefunc (*BigQuerySource) Reset
func (x *BigQuerySource) Reset()func (*BigQuerySource) String
func (x *BigQuerySource) String() stringBoundingBoxMetricsEntry
type BoundingBoxMetricsEntry struct {
IouThreshold float32 `protobuf:"fixed32,1,opt,name=iou_threshold,json=iouThreshold,proto3" json:"iou_threshold,omitempty"`
MeanAveragePrecision float32 `protobuf:"fixed32,2,opt,name=mean_average_precision,json=meanAveragePrecision,proto3" json:"mean_average_precision,omitempty"`
ConfidenceMetricsEntries []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry "" /* 135 byte string literal not displayed */
}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)Deprecated: Use BoundingBoxMetricsEntry.ProtoReflect.Descriptor instead.
func (*BoundingBoxMetricsEntry) GetConfidenceMetricsEntries
func (x *BoundingBoxMetricsEntry) GetConfidenceMetricsEntries() []*BoundingBoxMetricsEntry_ConfidenceMetricsEntryfunc (*BoundingBoxMetricsEntry) GetIouThreshold
func (x *BoundingBoxMetricsEntry) GetIouThreshold() float32func (*BoundingBoxMetricsEntry) GetMeanAveragePrecision
func (x *BoundingBoxMetricsEntry) GetMeanAveragePrecision() float32func (*BoundingBoxMetricsEntry) ProtoMessage
func (*BoundingBoxMetricsEntry) ProtoMessage()func (*BoundingBoxMetricsEntry) ProtoReflect
func (x *BoundingBoxMetricsEntry) ProtoReflect() protoreflect.Messagefunc (*BoundingBoxMetricsEntry) Reset
func (x *BoundingBoxMetricsEntry) Reset()func (*BoundingBoxMetricsEntry) String
func (x *BoundingBoxMetricsEntry) String() stringBoundingBoxMetricsEntry_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"`
// contains filtered or unexported fields
}Metrics for a single confidence threshold.
func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Descriptor
func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)Deprecated: Use BoundingBoxMetricsEntry_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.
func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold
func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold() float32func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score
func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score() float32func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision
func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision() float32func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall
func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall() float32func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage
func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage()func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect
func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Messagefunc (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset
func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset()func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String
func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String() stringBoundingPoly
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"`
// contains filtered or unexported fields
}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)Deprecated: Use BoundingPoly.ProtoReflect.Descriptor instead.
func (*BoundingPoly) GetNormalizedVertices
func (x *BoundingPoly) GetNormalizedVertices() []*NormalizedVertexfunc (*BoundingPoly) ProtoMessage
func (*BoundingPoly) ProtoMessage()func (*BoundingPoly) ProtoReflect
func (x *BoundingPoly) ProtoReflect() protoreflect.Messagefunc (*BoundingPoly) Reset
func (x *BoundingPoly) Reset()func (*BoundingPoly) String
func (x *BoundingPoly) String() stringCategoryStats
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"`
// contains filtered or unexported fields
}The data statistics of a series of CATEGORY values.
func (*CategoryStats) Descriptor
func (*CategoryStats) Descriptor() ([]byte, []int)Deprecated: Use CategoryStats.ProtoReflect.Descriptor instead.
func (*CategoryStats) GetTopCategoryStats
func (x *CategoryStats) GetTopCategoryStats() []*CategoryStats_SingleCategoryStatsfunc (*CategoryStats) ProtoMessage
func (*CategoryStats) ProtoMessage()func (*CategoryStats) ProtoReflect
func (x *CategoryStats) ProtoReflect() protoreflect.Messagefunc (*CategoryStats) Reset
func (x *CategoryStats) Reset()func (*CategoryStats) String
func (x *CategoryStats) String() stringCategoryStats_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"`
// contains filtered or unexported fields
}The statistics of a single CATEGORY value.
func (*CategoryStats_SingleCategoryStats) Descriptor
func (*CategoryStats_SingleCategoryStats) Descriptor() ([]byte, []int)Deprecated: Use CategoryStats_SingleCategoryStats.ProtoReflect.Descriptor instead.
func (*CategoryStats_SingleCategoryStats) GetCount
func (x *CategoryStats_SingleCategoryStats) GetCount() int64func (*CategoryStats_SingleCategoryStats) GetValue
func (x *CategoryStats_SingleCategoryStats) GetValue() stringfunc (*CategoryStats_SingleCategoryStats) ProtoMessage
func (*CategoryStats_SingleCategoryStats) ProtoMessage()func (*CategoryStats_SingleCategoryStats) ProtoReflect
func (x *CategoryStats_SingleCategoryStats) ProtoReflect() protoreflect.Messagefunc (*CategoryStats_SingleCategoryStats) Reset
func (x *CategoryStats_SingleCategoryStats) Reset()func (*CategoryStats_SingleCategoryStats) String
func (x *CategoryStats_SingleCategoryStats) String() stringClassificationAnnotation
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"`
// contains filtered or unexported fields
}Contains annotation details specific to classification.
func (*ClassificationAnnotation) Descriptor
func (*ClassificationAnnotation) Descriptor() ([]byte, []int)Deprecated: Use ClassificationAnnotation.ProtoReflect.Descriptor instead.
func (*ClassificationAnnotation) GetScore
func (x *ClassificationAnnotation) GetScore() float32func (*ClassificationAnnotation) ProtoMessage
func (*ClassificationAnnotation) ProtoMessage()func (*ClassificationAnnotation) ProtoReflect
func (x *ClassificationAnnotation) ProtoReflect() protoreflect.Messagefunc (*ClassificationAnnotation) Reset
func (x *ClassificationAnnotation) Reset()func (*ClassificationAnnotation) String
func (x *ClassificationAnnotation) String() stringClassificationEvaluationMetrics
type ClassificationEvaluationMetrics struct {
AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`
BaseAuPrc float32 `protobuf:"fixed32,2,opt,name=base_au_prc,json=baseAuPrc,proto3" json:"base_au_prc,omitempty"`
AuRoc float32 `protobuf:"fixed32,6,opt,name=au_roc,json=auRoc,proto3" json:"au_roc,omitempty"`
LogLoss float32 `protobuf:"fixed32,7,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"`
ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry "" /* 129 byte string literal not displayed */
ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`
AnnotationSpecId []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
}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)Deprecated: Use ClassificationEvaluationMetrics.ProtoReflect.Descriptor instead.
func (*ClassificationEvaluationMetrics) GetAnnotationSpecId
func (x *ClassificationEvaluationMetrics) GetAnnotationSpecId() []stringfunc (*ClassificationEvaluationMetrics) GetAuPrc
func (x *ClassificationEvaluationMetrics) GetAuPrc() float32func (*ClassificationEvaluationMetrics) GetAuRoc
func (x *ClassificationEvaluationMetrics) GetAuRoc() float32func (*ClassificationEvaluationMetrics) GetBaseAuPrc
func (x *ClassificationEvaluationMetrics) GetBaseAuPrc() float32Deprecated: Marked as deprecated in google/cloud/automl/v1beta1/classification.proto.
func (*ClassificationEvaluationMetrics) GetConfidenceMetricsEntry
func (x *ClassificationEvaluationMetrics) GetConfidenceMetricsEntry() []*ClassificationEvaluationMetrics_ConfidenceMetricsEntryfunc (*ClassificationEvaluationMetrics) GetConfusionMatrix
func (x *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrixfunc (*ClassificationEvaluationMetrics) GetLogLoss
func (x *ClassificationEvaluationMetrics) GetLogLoss() float32func (*ClassificationEvaluationMetrics) ProtoMessage
func (*ClassificationEvaluationMetrics) ProtoMessage()func (*ClassificationEvaluationMetrics) ProtoReflect
func (x *ClassificationEvaluationMetrics) ProtoReflect() protoreflect.Messagefunc (*ClassificationEvaluationMetrics) Reset
func (x *ClassificationEvaluationMetrics) Reset()func (*ClassificationEvaluationMetrics) String
func (x *ClassificationEvaluationMetrics) String() stringClassificationEvaluationMetrics_ConfidenceMetricsEntry
type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct {
ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
PositionThreshold int32 `protobuf:"varint,14,opt,name=position_threshold,json=positionThreshold,proto3" json:"position_threshold,omitempty"`
Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`
FalsePositiveRate float32 `protobuf:"fixed32,8,opt,name=false_positive_rate,json=falsePositiveRate,proto3" json:"false_positive_rate,omitempty"`
F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"`
PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"`
FalsePositiveRateAt1 float32 "" /* 127 byte string literal not displayed */
F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"`
TruePositiveCount int64 `protobuf:"varint,10,opt,name=true_positive_count,json=truePositiveCount,proto3" json:"true_positive_count,omitempty"`
FalsePositiveCount int64 `protobuf:"varint,11,opt,name=false_positive_count,json=falsePositiveCount,proto3" json:"false_positive_count,omitempty"`
FalseNegativeCount int64 `protobuf:"varint,12,opt,name=false_negative_count,json=falseNegativeCount,proto3" json:"false_negative_count,omitempty"`
TrueNegativeCount int64 `protobuf:"varint,13,opt,name=true_negative_count,json=trueNegativeCount,proto3" json:"true_negative_count,omitempty"`
}Metrics for a single confidence threshold.
func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor
func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)Deprecated: Use ClassificationEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.
func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount() int64func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount() int64func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold() int32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount() int64func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount() int64func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage
func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Messagefunc (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset()func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() stringClassificationEvaluationMetrics_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"`
// contains filtered or unexported fields
}Confusion matrix of the model running the classification.
func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor
func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int)Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix.ProtoReflect.Descriptor instead.
func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId() []stringfunc (*ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName() []stringfunc (*ClassificationEvaluationMetrics_ConfusionMatrix) GetRow
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetRow() []*ClassificationEvaluationMetrics_ConfusionMatrix_Rowfunc (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage
func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage()func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect() protoreflect.Messagefunc (*ClassificationEvaluationMetrics_ConfusionMatrix) Reset
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) Reset()func (*ClassificationEvaluationMetrics_ConfusionMatrix) String
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) String() stringClassificationEvaluationMetrics_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"`
// contains filtered or unexported fields
}Output only. A row in the confusion matrix.
func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor
func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int)Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix_Row.ProtoReflect.Descriptor instead.
func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount
func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount() []int32func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage
func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage()func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect
func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect() protoreflect.Messagefunc (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset
func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset()func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) String
func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() stringClassificationType
type ClassificationType int32Type of the classification problem.
ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED, ClassificationType_MULTICLASS, ClassificationType_MULTILABEL
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) Descriptor
func (ClassificationType) Descriptor() protoreflect.EnumDescriptorfunc (ClassificationType) Enum
func (x ClassificationType) Enum() *ClassificationTypefunc (ClassificationType) EnumDescriptor
func (ClassificationType) EnumDescriptor() ([]byte, []int)Deprecated: Use ClassificationType.Descriptor instead.
func (ClassificationType) Number
func (x ClassificationType) Number() protoreflect.EnumNumberfunc (ClassificationType) String
func (x ClassificationType) String() stringfunc (ClassificationType) Type
func (ClassificationType) Type() protoreflect.EnumTypeColumnSpec
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"`
// contains filtered or unexported fields
}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)Deprecated: Use ColumnSpec.ProtoReflect.Descriptor instead.
func (*ColumnSpec) GetDataStats
func (x *ColumnSpec) GetDataStats() *DataStatsfunc (*ColumnSpec) GetDataType
func (x *ColumnSpec) GetDataType() *DataTypefunc (*ColumnSpec) GetDisplayName
func (x *ColumnSpec) GetDisplayName() stringfunc (*ColumnSpec) GetEtag
func (x *ColumnSpec) GetEtag() stringfunc (*ColumnSpec) GetName
func (x *ColumnSpec) GetName() stringfunc (*ColumnSpec) GetTopCorrelatedColumns
func (x *ColumnSpec) GetTopCorrelatedColumns() []*ColumnSpec_CorrelatedColumnfunc (*ColumnSpec) ProtoMessage
func (*ColumnSpec) ProtoMessage()func (*ColumnSpec) ProtoReflect
func (x *ColumnSpec) ProtoReflect() protoreflect.Messagefunc (*ColumnSpec) Reset
func (x *ColumnSpec) Reset()func (*ColumnSpec) String
func (x *ColumnSpec) String() stringColumnSpec_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"`
// contains filtered or unexported fields
}Identifies the table's column, and its correlation with the column this ColumnSpec describes.
func (*ColumnSpec_CorrelatedColumn) Descriptor
func (*ColumnSpec_CorrelatedColumn) Descriptor() ([]byte, []int)Deprecated: Use ColumnSpec_CorrelatedColumn.ProtoReflect.Descriptor instead.
func (*ColumnSpec_CorrelatedColumn) GetColumnSpecId
func (x *ColumnSpec_CorrelatedColumn) GetColumnSpecId() stringfunc (*ColumnSpec_CorrelatedColumn) GetCorrelationStats
func (x *ColumnSpec_CorrelatedColumn) GetCorrelationStats() *CorrelationStatsfunc (*ColumnSpec_CorrelatedColumn) ProtoMessage
func (*ColumnSpec_CorrelatedColumn) ProtoMessage()func (*ColumnSpec_CorrelatedColumn) ProtoReflect
func (x *ColumnSpec_CorrelatedColumn) ProtoReflect() protoreflect.Messagefunc (*ColumnSpec_CorrelatedColumn) Reset
func (x *ColumnSpec_CorrelatedColumn) Reset()func (*ColumnSpec_CorrelatedColumn) String
func (x *ColumnSpec_CorrelatedColumn) String() stringCorrelationStats
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"`
// contains filtered or unexported fields
}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)Deprecated: Use CorrelationStats.ProtoReflect.Descriptor instead.
func (*CorrelationStats) GetCramersV
func (x *CorrelationStats) GetCramersV() float64func (*CorrelationStats) ProtoMessage
func (*CorrelationStats) ProtoMessage()func (*CorrelationStats) ProtoReflect
func (x *CorrelationStats) ProtoReflect() protoreflect.Messagefunc (*CorrelationStats) Reset
func (x *CorrelationStats) Reset()func (*CorrelationStats) String
func (x *CorrelationStats) String() stringCreateDatasetRequest
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"`
// contains filtered or unexported fields
}Request message for [AutoMl.CreateDataset][google.cloud.automl.v1beta1.AutoMl.CreateDataset].
func (*CreateDatasetRequest) Descriptor
func (*CreateDatasetRequest) Descriptor() ([]byte, []int)Deprecated: Use CreateDatasetRequest.ProtoReflect.Descriptor instead.
func (*CreateDatasetRequest) GetDataset
func (x *CreateDatasetRequest) GetDataset() *Datasetfunc (*CreateDatasetRequest) GetParent
func (x *CreateDatasetRequest) GetParent() stringfunc (*CreateDatasetRequest) ProtoMessage
func (*CreateDatasetRequest) ProtoMessage()func (*CreateDatasetRequest) ProtoReflect
func (x *CreateDatasetRequest) ProtoReflect() protoreflect.Messagefunc (*CreateDatasetRequest) Reset
func (x *CreateDatasetRequest) Reset()func (*CreateDatasetRequest) String
func (x *CreateDatasetRequest) String() stringCreateModelOperationMetadata
type CreateModelOperationMetadata struct {
// contains filtered or unexported fields
}Details of CreateModel operation.
func (*CreateModelOperationMetadata) Descriptor
func (*CreateModelOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use CreateModelOperationMetadata.ProtoReflect.Descriptor instead.
func (*CreateModelOperationMetadata) ProtoMessage
func (*CreateModelOperationMetadata) ProtoMessage()func (*CreateModelOperationMetadata) ProtoReflect
func (x *CreateModelOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*CreateModelOperationMetadata) Reset
func (x *CreateModelOperationMetadata) Reset()func (*CreateModelOperationMetadata) String
func (x *CreateModelOperationMetadata) String() stringCreateModelRequest
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"`
// contains filtered or unexported fields
}Request message for [AutoMl.CreateModel][google.cloud.automl.v1beta1.AutoMl.CreateModel].
func (*CreateModelRequest) Descriptor
func (*CreateModelRequest) Descriptor() ([]byte, []int)Deprecated: Use CreateModelRequest.ProtoReflect.Descriptor instead.
func (*CreateModelRequest) GetModel
func (x *CreateModelRequest) GetModel() *Modelfunc (*CreateModelRequest) GetParent
func (x *CreateModelRequest) GetParent() stringfunc (*CreateModelRequest) ProtoMessage
func (*CreateModelRequest) ProtoMessage()func (*CreateModelRequest) ProtoReflect
func (x *CreateModelRequest) ProtoReflect() protoreflect.Messagefunc (*CreateModelRequest) Reset
func (x *CreateModelRequest) Reset()func (*CreateModelRequest) String
func (x *CreateModelRequest) String() stringDataStats
type DataStats struct {
// The data statistics specific to a DataType.
//
// Types that are assignable 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"`
// contains filtered or unexported fields
}The data statistics of a series of values that share the same DataType.
func (*DataStats) Descriptor
Deprecated: Use DataStats.ProtoReflect.Descriptor instead.
func (*DataStats) GetArrayStats
func (x *DataStats) GetArrayStats() *ArrayStatsfunc (*DataStats) GetCategoryStats
func (x *DataStats) GetCategoryStats() *CategoryStatsfunc (*DataStats) GetDistinctValueCount
func (*DataStats) GetFloat64Stats
func (x *DataStats) GetFloat64Stats() *Float64Statsfunc (*DataStats) GetNullValueCount
func (*DataStats) GetStats
func (m *DataStats) GetStats() isDataStats_Statsfunc (*DataStats) GetStringStats
func (x *DataStats) GetStringStats() *StringStatsfunc (*DataStats) GetStructStats
func (x *DataStats) GetStructStats() *StructStatsfunc (*DataStats) GetTimestampStats
func (x *DataStats) GetTimestampStats() *TimestampStatsfunc (*DataStats) GetValidValueCount
func (*DataStats) ProtoMessage
func (*DataStats) ProtoMessage()func (*DataStats) ProtoReflect
func (x *DataStats) ProtoReflect() protoreflect.Messagefunc (*DataStats) Reset
func (x *DataStats) Reset()func (*DataStats) String
DataStats_ArrayStats
type DataStats_ArrayStats struct {
// The statistics for ARRAY DataType.
ArrayStats *ArrayStats `protobuf:"bytes,6,opt,name=array_stats,json=arrayStats,proto3,oneof"`
}DataStats_CategoryStats
type DataStats_CategoryStats struct {
// The statistics for CATEGORY DataType.
CategoryStats *CategoryStats `protobuf:"bytes,8,opt,name=category_stats,json=categoryStats,proto3,oneof"`
}DataStats_Float64Stats
type DataStats_Float64Stats struct {
// The statistics for FLOAT64 DataType.
Float64Stats *Float64Stats `protobuf:"bytes,3,opt,name=float64_stats,json=float64Stats,proto3,oneof"`
}DataStats_StringStats
type DataStats_StringStats struct {
// The statistics for STRING DataType.
StringStats *StringStats `protobuf:"bytes,4,opt,name=string_stats,json=stringStats,proto3,oneof"`
}DataStats_StructStats
type DataStats_StructStats struct {
// The statistics for STRUCT DataType.
StructStats *StructStats `protobuf:"bytes,7,opt,name=struct_stats,json=structStats,proto3,oneof"`
}DataStats_TimestampStats
type DataStats_TimestampStats struct {
// The statistics for TIMESTAMP DataType.
TimestampStats *TimestampStats `protobuf:"bytes,5,opt,name=timestamp_stats,json=timestampStats,proto3,oneof"`
}DataType
type DataType struct {
Details isDataType_Details `protobuf_oneof:"details"`
TypeCode TypeCode "" /* 128 byte string literal not displayed */
Nullable bool `protobuf:"varint,4,opt,name=nullable,proto3" json:"nullable,omitempty"`
}Indicated the type of data that can be stored in a structured data entity (e.g. a table).
func (*DataType) Descriptor
Deprecated: Use DataType.ProtoReflect.Descriptor instead.
func (*DataType) GetDetails
func (m *DataType) GetDetails() isDataType_Detailsfunc (*DataType) GetListElementType
func (*DataType) GetNullable
func (*DataType) GetStructType
func (x *DataType) GetStructType() *StructTypefunc (*DataType) GetTimeFormat
func (*DataType) GetTypeCode
func (*DataType) ProtoMessage
func (*DataType) ProtoMessage()func (*DataType) ProtoReflect
func (x *DataType) ProtoReflect() protoreflect.Messagefunc (*DataType) Reset
func (x *DataType) Reset()func (*DataType) String
DataType_ListElementType
type DataType_ListElementType struct {
// If [type_code][google.cloud.automl.v1beta1.DataType.type_code] == [ARRAY][google.cloud.automl.v1beta1.TypeCode.ARRAY],
// then `list_element_type` is the type of the elements.
ListElementType *DataType `protobuf:"bytes,2,opt,name=list_element_type,json=listElementType,proto3,oneof"`
}DataType_StructType
type DataType_StructType struct {
// If [type_code][google.cloud.automl.v1beta1.DataType.type_code] == [STRUCT][google.cloud.automl.v1beta1.TypeCode.STRUCT], then `struct_type`
// provides type information for the struct's fields.
StructType *StructType `protobuf:"bytes,3,opt,name=struct_type,json=structType,proto3,oneof"`
}DataType_TimeFormat
type DataType_TimeFormat struct {
// If [type_code][google.cloud.automl.v1beta1.DataType.type_code] == [TIMESTAMP][google.cloud.automl.v1beta1.TypeCode.TIMESTAMP]
// then `time_format` provides the format in which that time field is
// expressed. The time_format must either be one of:
// * `UNIX_SECONDS`
// * `UNIX_MILLISECONDS`
// * `UNIX_MICROSECONDS`
// * `UNIX_NANOSECONDS`
// (for respectively number of seconds, milliseconds, microseconds and
// nanoseconds since start of the Unix epoch);
// or be written in `strftime` syntax. If time_format is not set, then the
// default format as described on the type_code is used.
TimeFormat string `protobuf:"bytes,5,opt,name=time_format,json=timeFormat,proto3,oneof"`
}Dataset
type Dataset struct {
// Required.
// The dataset metadata that is specific to the problem type.
//
// Types that are assignable 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 *timestamppb.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"`
// contains filtered or unexported fields
}A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
func (*Dataset) Descriptor
Deprecated: Use Dataset.ProtoReflect.Descriptor instead.
func (*Dataset) GetCreateTime
func (x *Dataset) GetCreateTime() *timestamppb.Timestampfunc (*Dataset) GetDatasetMetadata
func (m *Dataset) GetDatasetMetadata() isDataset_DatasetMetadatafunc (*Dataset) GetDescription
func (*Dataset) GetDisplayName
func (*Dataset) GetEtag
func (*Dataset) GetExampleCount
func (*Dataset) GetImageClassificationDatasetMetadata
func (x *Dataset) GetImageClassificationDatasetMetadata() *ImageClassificationDatasetMetadatafunc (*Dataset) GetImageObjectDetectionDatasetMetadata
func (x *Dataset) GetImageObjectDetectionDatasetMetadata() *ImageObjectDetectionDatasetMetadatafunc (*Dataset) GetName
func (*Dataset) GetTablesDatasetMetadata
func (x *Dataset) GetTablesDatasetMetadata() *TablesDatasetMetadatafunc (*Dataset) GetTextClassificationDatasetMetadata
func (x *Dataset) GetTextClassificationDatasetMetadata() *TextClassificationDatasetMetadatafunc (*Dataset) GetTextExtractionDatasetMetadata
func (x *Dataset) GetTextExtractionDatasetMetadata() *TextExtractionDatasetMetadatafunc (*Dataset) GetTextSentimentDatasetMetadata
func (x *Dataset) GetTextSentimentDatasetMetadata() *TextSentimentDatasetMetadatafunc (*Dataset) GetTranslationDatasetMetadata
func (x *Dataset) GetTranslationDatasetMetadata() *TranslationDatasetMetadatafunc (*Dataset) GetVideoClassificationDatasetMetadata
func (x *Dataset) GetVideoClassificationDatasetMetadata() *VideoClassificationDatasetMetadatafunc (*Dataset) GetVideoObjectTrackingDatasetMetadata
func (x *Dataset) GetVideoObjectTrackingDatasetMetadata() *VideoObjectTrackingDatasetMetadatafunc (*Dataset) ProtoMessage
func (*Dataset) ProtoMessage()func (*Dataset) ProtoReflect
func (x *Dataset) ProtoReflect() protoreflect.Messagefunc (*Dataset) Reset
func (x *Dataset) Reset()func (*Dataset) String
Dataset_ImageClassificationDatasetMetadata
type Dataset_ImageClassificationDatasetMetadata struct {
// Metadata for a dataset used for image classification.
ImageClassificationDatasetMetadata *ImageClassificationDatasetMetadata `protobuf:"bytes,24,opt,name=image_classification_dataset_metadata,json=imageClassificationDatasetMetadata,proto3,oneof"`
}Dataset_ImageObjectDetectionDatasetMetadata
type Dataset_ImageObjectDetectionDatasetMetadata struct {
// Metadata for a dataset used for image object detection.
ImageObjectDetectionDatasetMetadata *ImageObjectDetectionDatasetMetadata `protobuf:"bytes,26,opt,name=image_object_detection_dataset_metadata,json=imageObjectDetectionDatasetMetadata,proto3,oneof"`
}Dataset_TablesDatasetMetadata
type Dataset_TablesDatasetMetadata struct {
// Metadata for a dataset used for Tables.
TablesDatasetMetadata *TablesDatasetMetadata `protobuf:"bytes,33,opt,name=tables_dataset_metadata,json=tablesDatasetMetadata,proto3,oneof"`
}Dataset_TextClassificationDatasetMetadata
type Dataset_TextClassificationDatasetMetadata struct {
// Metadata for a dataset used for text classification.
TextClassificationDatasetMetadata *TextClassificationDatasetMetadata `protobuf:"bytes,25,opt,name=text_classification_dataset_metadata,json=textClassificationDatasetMetadata,proto3,oneof"`
}Dataset_TextExtractionDatasetMetadata
type Dataset_TextExtractionDatasetMetadata struct {
// Metadata for a dataset used for text extraction.
TextExtractionDatasetMetadata *TextExtractionDatasetMetadata `protobuf:"bytes,28,opt,name=text_extraction_dataset_metadata,json=textExtractionDatasetMetadata,proto3,oneof"`
}Dataset_TextSentimentDatasetMetadata
type Dataset_TextSentimentDatasetMetadata struct {
// Metadata for a dataset used for text sentiment.
TextSentimentDatasetMetadata *TextSentimentDatasetMetadata `protobuf:"bytes,30,opt,name=text_sentiment_dataset_metadata,json=textSentimentDatasetMetadata,proto3,oneof"`
}Dataset_TranslationDatasetMetadata
type Dataset_TranslationDatasetMetadata struct {
// Metadata for a dataset used for translation.
TranslationDatasetMetadata *TranslationDatasetMetadata `protobuf:"bytes,23,opt,name=translation_dataset_metadata,json=translationDatasetMetadata,proto3,oneof"`
}Dataset_VideoClassificationDatasetMetadata
type Dataset_VideoClassificationDatasetMetadata struct {
// Metadata for a dataset used for video classification.
VideoClassificationDatasetMetadata *VideoClassificationDatasetMetadata `protobuf:"bytes,31,opt,name=video_classification_dataset_metadata,json=videoClassificationDatasetMetadata,proto3,oneof"`
}Dataset_VideoObjectTrackingDatasetMetadata
type Dataset_VideoObjectTrackingDatasetMetadata struct {
// Metadata for a dataset used for video object tracking.
VideoObjectTrackingDatasetMetadata *VideoObjectTrackingDatasetMetadata `protobuf:"bytes,29,opt,name=video_object_tracking_dataset_metadata,json=videoObjectTrackingDatasetMetadata,proto3,oneof"`
}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"`
// contains filtered or unexported fields
}Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1beta1.AutoMl.DeleteDataset].
func (*DeleteDatasetRequest) Descriptor
func (*DeleteDatasetRequest) Descriptor() ([]byte, []int)Deprecated: Use DeleteDatasetRequest.ProtoReflect.Descriptor instead.
func (*DeleteDatasetRequest) GetName
func (x *DeleteDatasetRequest) GetName() stringfunc (*DeleteDatasetRequest) ProtoMessage
func (*DeleteDatasetRequest) ProtoMessage()func (*DeleteDatasetRequest) ProtoReflect
func (x *DeleteDatasetRequest) ProtoReflect() protoreflect.Messagefunc (*DeleteDatasetRequest) Reset
func (x *DeleteDatasetRequest) Reset()func (*DeleteDatasetRequest) String
func (x *DeleteDatasetRequest) String() stringDeleteModelRequest
type DeleteModelRequest struct {
// Required. Resource name of the model being deleted.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.DeleteModel][google.cloud.automl.v1beta1.AutoMl.DeleteModel].
func (*DeleteModelRequest) Descriptor
func (*DeleteModelRequest) Descriptor() ([]byte, []int)Deprecated: Use DeleteModelRequest.ProtoReflect.Descriptor instead.
func (*DeleteModelRequest) GetName
func (x *DeleteModelRequest) GetName() stringfunc (*DeleteModelRequest) ProtoMessage
func (*DeleteModelRequest) ProtoMessage()func (*DeleteModelRequest) ProtoReflect
func (x *DeleteModelRequest) ProtoReflect() protoreflect.Messagefunc (*DeleteModelRequest) Reset
func (x *DeleteModelRequest) Reset()func (*DeleteModelRequest) String
func (x *DeleteModelRequest) String() stringDeleteOperationMetadata
type DeleteOperationMetadata struct {
// contains filtered or unexported fields
}Details of operations that perform deletes of any entities.
func (*DeleteOperationMetadata) Descriptor
func (*DeleteOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use DeleteOperationMetadata.ProtoReflect.Descriptor instead.
func (*DeleteOperationMetadata) ProtoMessage
func (*DeleteOperationMetadata) ProtoMessage()func (*DeleteOperationMetadata) ProtoReflect
func (x *DeleteOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*DeleteOperationMetadata) Reset
func (x *DeleteOperationMetadata) Reset()func (*DeleteOperationMetadata) String
func (x *DeleteOperationMetadata) String() stringDeployModelOperationMetadata
type DeployModelOperationMetadata struct {
// contains filtered or unexported fields
}Details of DeployModel operation.
func (*DeployModelOperationMetadata) Descriptor
func (*DeployModelOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use DeployModelOperationMetadata.ProtoReflect.Descriptor instead.
func (*DeployModelOperationMetadata) ProtoMessage
func (*DeployModelOperationMetadata) ProtoMessage()func (*DeployModelOperationMetadata) ProtoReflect
func (x *DeployModelOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*DeployModelOperationMetadata) Reset
func (x *DeployModelOperationMetadata) Reset()func (*DeployModelOperationMetadata) String
func (x *DeployModelOperationMetadata) String() stringDeployModelRequest
type DeployModelRequest struct {
// The per-domain specific deployment parameters.
//
// Types that are assignable 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"`
// contains filtered or unexported fields
}Request message for [AutoMl.DeployModel][google.cloud.automl.v1beta1.AutoMl.DeployModel].
func (*DeployModelRequest) Descriptor
func (*DeployModelRequest) Descriptor() ([]byte, []int)Deprecated: Use DeployModelRequest.ProtoReflect.Descriptor instead.
func (*DeployModelRequest) GetImageClassificationModelDeploymentMetadata
func (x *DeployModelRequest) GetImageClassificationModelDeploymentMetadata() *ImageClassificationModelDeploymentMetadatafunc (*DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata
func (x *DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata() *ImageObjectDetectionModelDeploymentMetadatafunc (*DeployModelRequest) GetModelDeploymentMetadata
func (m *DeployModelRequest) GetModelDeploymentMetadata() isDeployModelRequest_ModelDeploymentMetadatafunc (*DeployModelRequest) GetName
func (x *DeployModelRequest) GetName() stringfunc (*DeployModelRequest) ProtoMessage
func (*DeployModelRequest) ProtoMessage()func (*DeployModelRequest) ProtoReflect
func (x *DeployModelRequest) ProtoReflect() protoreflect.Messagefunc (*DeployModelRequest) Reset
func (x *DeployModelRequest) Reset()func (*DeployModelRequest) String
func (x *DeployModelRequest) String() stringDeployModelRequest_ImageClassificationModelDeploymentMetadata
type DeployModelRequest_ImageClassificationModelDeploymentMetadata struct {
ImageClassificationModelDeploymentMetadata *ImageClassificationModelDeploymentMetadata "" /* 135 byte string literal not displayed */
}DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata
type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata struct {
ImageObjectDetectionModelDeploymentMetadata *ImageObjectDetectionModelDeploymentMetadata "" /* 138 byte string literal not displayed */
}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"`
// contains filtered or unexported fields
}A structured text document e.g. a PDF.
func (*Document) Descriptor
Deprecated: Use Document.ProtoReflect.Descriptor instead.
func (*Document) GetDocumentDimensions
func (x *Document) GetDocumentDimensions() *DocumentDimensionsfunc (*Document) GetDocumentText
func (x *Document) GetDocumentText() *TextSnippetfunc (*Document) GetInputConfig
func (x *Document) GetInputConfig() *DocumentInputConfigfunc (*Document) GetLayout
func (x *Document) GetLayout() []*Document_Layoutfunc (*Document) GetPageCount
func (*Document) ProtoMessage
func (*Document) ProtoMessage()func (*Document) ProtoReflect
func (x *Document) ProtoReflect() protoreflect.Messagefunc (*Document) Reset
func (x *Document) Reset()func (*Document) String
DocumentDimensions
type DocumentDimensions struct {
Unit DocumentDimensions_DocumentDimensionUnit "" /* 136 byte string literal not displayed */
Width float32 `protobuf:"fixed32,2,opt,name=width,proto3" json:"width,omitempty"`
Height float32 `protobuf:"fixed32,3,opt,name=height,proto3" json:"height,omitempty"`
}Message that describes dimension of a document.
func (*DocumentDimensions) Descriptor
func (*DocumentDimensions) Descriptor() ([]byte, []int)Deprecated: Use DocumentDimensions.ProtoReflect.Descriptor instead.
func (*DocumentDimensions) GetHeight
func (x *DocumentDimensions) GetHeight() float32func (*DocumentDimensions) GetUnit
func (x *DocumentDimensions) GetUnit() DocumentDimensions_DocumentDimensionUnitfunc (*DocumentDimensions) GetWidth
func (x *DocumentDimensions) GetWidth() float32func (*DocumentDimensions) ProtoMessage
func (*DocumentDimensions) ProtoMessage()func (*DocumentDimensions) ProtoReflect
func (x *DocumentDimensions) ProtoReflect() protoreflect.Messagefunc (*DocumentDimensions) Reset
func (x *DocumentDimensions) Reset()func (*DocumentDimensions) String
func (x *DocumentDimensions) String() stringDocumentDimensions_DocumentDimensionUnit
type DocumentDimensions_DocumentDimensionUnit int32Unit of the document dimension.
DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED, DocumentDimensions_INCH, DocumentDimensions_CENTIMETER, DocumentDimensions_POINT
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) Descriptor
func (DocumentDimensions_DocumentDimensionUnit) Descriptor() protoreflect.EnumDescriptorfunc (DocumentDimensions_DocumentDimensionUnit) Enum
func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor
func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor() ([]byte, []int)Deprecated: Use DocumentDimensions_DocumentDimensionUnit.Descriptor instead.
func (DocumentDimensions_DocumentDimensionUnit) Number
func (x DocumentDimensions_DocumentDimensionUnit) Number() protoreflect.EnumNumberfunc (DocumentDimensions_DocumentDimensionUnit) String
func (x DocumentDimensions_DocumentDimensionUnit) String() stringfunc (DocumentDimensions_DocumentDimensionUnit) Type
func (DocumentDimensions_DocumentDimensionUnit) Type() protoreflect.EnumTypeDocumentInputConfig
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"`
// contains filtered or unexported fields
}Input configuration of a [Document][google.cloud.automl.v1beta1.Document].
func (*DocumentInputConfig) Descriptor
func (*DocumentInputConfig) Descriptor() ([]byte, []int)Deprecated: Use DocumentInputConfig.ProtoReflect.Descriptor instead.
func (*DocumentInputConfig) GetGcsSource
func (x *DocumentInputConfig) GetGcsSource() *GcsSourcefunc (*DocumentInputConfig) ProtoMessage
func (*DocumentInputConfig) ProtoMessage()func (*DocumentInputConfig) ProtoReflect
func (x *DocumentInputConfig) ProtoReflect() protoreflect.Messagefunc (*DocumentInputConfig) Reset
func (x *DocumentInputConfig) Reset()func (*DocumentInputConfig) String
func (x *DocumentInputConfig) String() stringDocument_Layout
type Document_Layout struct {
TextSegment *TextSegment `protobuf:"bytes,1,opt,name=text_segment,json=textSegment,proto3" json:"text_segment,omitempty"`
PageNumber int32 `protobuf:"varint,2,opt,name=page_number,json=pageNumber,proto3" json:"page_number,omitempty"`
BoundingPoly *BoundingPoly `protobuf:"bytes,3,opt,name=bounding_poly,json=boundingPoly,proto3" json:"bounding_poly,omitempty"`
TextSegmentType Document_Layout_TextSegmentType "" /* 174 byte string literal not displayed */
}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)Deprecated: Use Document_Layout.ProtoReflect.Descriptor instead.
func (*Document_Layout) GetBoundingPoly
func (x *Document_Layout) GetBoundingPoly() *BoundingPolyfunc (*Document_Layout) GetPageNumber
func (x *Document_Layout) GetPageNumber() int32func (*Document_Layout) GetTextSegment
func (x *Document_Layout) GetTextSegment() *TextSegmentfunc (*Document_Layout) GetTextSegmentType
func (x *Document_Layout) GetTextSegmentType() Document_Layout_TextSegmentTypefunc (*Document_Layout) ProtoMessage
func (*Document_Layout) ProtoMessage()func (*Document_Layout) ProtoReflect
func (x *Document_Layout) ProtoReflect() protoreflect.Messagefunc (*Document_Layout) Reset
func (x *Document_Layout) Reset()func (*Document_Layout) String
func (x *Document_Layout) String() stringDocument_Layout_TextSegmentType
type Document_Layout_TextSegmentType int32The type of TextSegment in the context of the original document.
Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED, Document_Layout_TOKEN, Document_Layout_PARAGRAPH, Document_Layout_FORM_FIELD, Document_Layout_FORM_FIELD_NAME, Document_Layout_FORM_FIELD_CONTENTS, Document_Layout_TABLE, Document_Layout_TABLE_HEADER, Document_Layout_TABLE_ROW, Document_Layout_TABLE_CELL
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) Descriptor
func (Document_Layout_TextSegmentType) Descriptor() protoreflect.EnumDescriptorfunc (Document_Layout_TextSegmentType) Enum
func (x Document_Layout_TextSegmentType) Enum() *Document_Layout_TextSegmentTypefunc (Document_Layout_TextSegmentType) EnumDescriptor
func (Document_Layout_TextSegmentType) EnumDescriptor() ([]byte, []int)Deprecated: Use Document_Layout_TextSegmentType.Descriptor instead.
func (Document_Layout_TextSegmentType) Number
func (x Document_Layout_TextSegmentType) Number() protoreflect.EnumNumberfunc (Document_Layout_TextSegmentType) String
func (x Document_Layout_TextSegmentType) String() stringfunc (Document_Layout_TextSegmentType) Type
func (Document_Layout_TextSegmentType) Type() protoreflect.EnumTypeDoubleRange
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"`
// contains filtered or unexported fields
}A range between two double numbers.
func (*DoubleRange) Descriptor
func (*DoubleRange) Descriptor() ([]byte, []int)Deprecated: Use DoubleRange.ProtoReflect.Descriptor instead.
func (*DoubleRange) GetEnd
func (x *DoubleRange) GetEnd() float64func (*DoubleRange) GetStart
func (x *DoubleRange) GetStart() float64func (*DoubleRange) ProtoMessage
func (*DoubleRange) ProtoMessage()func (*DoubleRange) ProtoReflect
func (x *DoubleRange) ProtoReflect() protoreflect.Messagefunc (*DoubleRange) Reset
func (x *DoubleRange) Reset()func (*DoubleRange) String
func (x *DoubleRange) String() stringExamplePayload
type ExamplePayload struct {
// Required. Input only. The example data.
//
// Types that are assignable to Payload:
// *ExamplePayload_Image
// *ExamplePayload_TextSnippet
// *ExamplePayload_Document
// *ExamplePayload_Row
Payload isExamplePayload_Payload `protobuf_oneof:"payload"`
// contains filtered or unexported fields
}Example data used for training or prediction.
func (*ExamplePayload) Descriptor
func (*ExamplePayload) Descriptor() ([]byte, []int)Deprecated: Use ExamplePayload.ProtoReflect.Descriptor instead.
func (*ExamplePayload) GetDocument
func (x *ExamplePayload) GetDocument() *Documentfunc (*ExamplePayload) GetImage
func (x *ExamplePayload) GetImage() *Imagefunc (*ExamplePayload) GetPayload
func (m *ExamplePayload) GetPayload() isExamplePayload_Payloadfunc (*ExamplePayload) GetRow
func (x *ExamplePayload) GetRow() *Rowfunc (*ExamplePayload) GetTextSnippet
func (x *ExamplePayload) GetTextSnippet() *TextSnippetfunc (*ExamplePayload) ProtoMessage
func (*ExamplePayload) ProtoMessage()func (*ExamplePayload) ProtoReflect
func (x *ExamplePayload) ProtoReflect() protoreflect.Messagefunc (*ExamplePayload) Reset
func (x *ExamplePayload) Reset()func (*ExamplePayload) String
func (x *ExamplePayload) String() stringExamplePayload_Document
type ExamplePayload_Document struct {
// Example document.
Document *Document `protobuf:"bytes,4,opt,name=document,proto3,oneof"`
}ExamplePayload_Image
type ExamplePayload_Image struct {
// Example image.
Image *Image `protobuf:"bytes,1,opt,name=image,proto3,oneof"`
}ExamplePayload_Row
type ExamplePayload_Row struct {
// Example relational table row.
Row *Row `protobuf:"bytes,3,opt,name=row,proto3,oneof"`
}ExamplePayload_TextSnippet
type ExamplePayload_TextSnippet struct {
// Example text.
TextSnippet *TextSnippet `protobuf:"bytes,2,opt,name=text_snippet,json=textSnippet,proto3,oneof"`
}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"`
// contains filtered or unexported fields
}Details of ExportData operation.
func (*ExportDataOperationMetadata) Descriptor
func (*ExportDataOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use ExportDataOperationMetadata.ProtoReflect.Descriptor instead.
func (*ExportDataOperationMetadata) GetOutputInfo
func (x *ExportDataOperationMetadata) GetOutputInfo() *ExportDataOperationMetadata_ExportDataOutputInfofunc (*ExportDataOperationMetadata) ProtoMessage
func (*ExportDataOperationMetadata) ProtoMessage()func (*ExportDataOperationMetadata) ProtoReflect
func (x *ExportDataOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*ExportDataOperationMetadata) Reset
func (x *ExportDataOperationMetadata) Reset()func (*ExportDataOperationMetadata) String
func (x *ExportDataOperationMetadata) String() stringExportDataOperationMetadata_ExportDataOutputInfo
type ExportDataOperationMetadata_ExportDataOutputInfo struct {
// The output location to which the exported data is written.
//
// Types that are assignable to OutputLocation:
// *ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory
// *ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset
OutputLocation isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation `protobuf_oneof:"output_location"`
// contains filtered or unexported fields
}Further describes this export data's output. Supplements [OutputConfig][google.cloud.automl.v1beta1.OutputConfig].
func (*ExportDataOperationMetadata_ExportDataOutputInfo) Descriptor
func (*ExportDataOperationMetadata_ExportDataOutputInfo) Descriptor() ([]byte, []int)Deprecated: Use ExportDataOperationMetadata_ExportDataOutputInfo.ProtoReflect.Descriptor instead.
func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetBigqueryOutputDataset
func (x *ExportDataOperationMetadata_ExportDataOutputInfo) GetBigqueryOutputDataset() stringfunc (*ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory
func (x *ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory() stringfunc (*ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation
func (m *ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation() isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocationfunc (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage
func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage()func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoReflect
func (x *ExportDataOperationMetadata_ExportDataOutputInfo) ProtoReflect() protoreflect.Messagefunc (*ExportDataOperationMetadata_ExportDataOutputInfo) Reset
func (x *ExportDataOperationMetadata_ExportDataOutputInfo) Reset()func (*ExportDataOperationMetadata_ExportDataOutputInfo) String
func (x *ExportDataOperationMetadata_ExportDataOutputInfo) String() stringExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset
type ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset struct {
// The path of the BigQuery dataset created, in bq://projectId.bqDatasetId
// format, into which the exported data is written.
BigqueryOutputDataset string `protobuf:"bytes,2,opt,name=bigquery_output_dataset,json=bigqueryOutputDataset,proto3,oneof"`
}ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory
type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory struct {
// The full path of the Google Cloud Storage directory created, into which
// the exported data is written.
GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3,oneof"`
}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"`
// contains filtered or unexported fields
}Request message for [AutoMl.ExportData][google.cloud.automl.v1beta1.AutoMl.ExportData].
func (*ExportDataRequest) Descriptor
func (*ExportDataRequest) Descriptor() ([]byte, []int)Deprecated: Use ExportDataRequest.ProtoReflect.Descriptor instead.
func (*ExportDataRequest) GetName
func (x *ExportDataRequest) GetName() stringfunc (*ExportDataRequest) GetOutputConfig
func (x *ExportDataRequest) GetOutputConfig() *OutputConfigfunc (*ExportDataRequest) ProtoMessage
func (*ExportDataRequest) ProtoMessage()func (*ExportDataRequest) ProtoReflect
func (x *ExportDataRequest) ProtoReflect() protoreflect.Messagefunc (*ExportDataRequest) Reset
func (x *ExportDataRequest) Reset()func (*ExportDataRequest) String
func (x *ExportDataRequest) String() stringExportEvaluatedExamplesOperationMetadata
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"`
// contains filtered or unexported fields
}Details of EvaluatedExamples operation.
func (*ExportEvaluatedExamplesOperationMetadata) Descriptor
func (*ExportEvaluatedExamplesOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use ExportEvaluatedExamplesOperationMetadata.ProtoReflect.Descriptor instead.
func (*ExportEvaluatedExamplesOperationMetadata) GetOutputInfo
func (x *ExportEvaluatedExamplesOperationMetadata) GetOutputInfo() *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfofunc (*ExportEvaluatedExamplesOperationMetadata) ProtoMessage
func (*ExportEvaluatedExamplesOperationMetadata) ProtoMessage()func (*ExportEvaluatedExamplesOperationMetadata) ProtoReflect
func (x *ExportEvaluatedExamplesOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*ExportEvaluatedExamplesOperationMetadata) Reset
func (x *ExportEvaluatedExamplesOperationMetadata) Reset()func (*ExportEvaluatedExamplesOperationMetadata) String
func (x *ExportEvaluatedExamplesOperationMetadata) String() stringExportEvaluatedExamplesOperationMetadata_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 `protobuf:"bytes,2,opt,name=bigquery_output_dataset,json=bigqueryOutputDataset,proto3" json:"bigquery_output_dataset,omitempty"`
// contains filtered or unexported fields
}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)Deprecated: Use ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo.ProtoReflect.Descriptor instead.
func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) GetBigqueryOutputDataset
func (x *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) GetBigqueryOutputDataset() stringfunc (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoMessage
func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoMessage()func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoReflect
func (x *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoReflect() protoreflect.Messagefunc (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) Reset
func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) String
func (x *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) String() stringExportEvaluatedExamplesOutputConfig
type ExportEvaluatedExamplesOutputConfig struct {
// Required. The destination of the output.
//
// Types that are assignable to Destination:
// *ExportEvaluatedExamplesOutputConfig_BigqueryDestination
Destination isExportEvaluatedExamplesOutputConfig_Destination `protobuf_oneof:"destination"`
// contains filtered or unexported fields
}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)Deprecated: Use ExportEvaluatedExamplesOutputConfig.ProtoReflect.Descriptor instead.
func (*ExportEvaluatedExamplesOutputConfig) GetBigqueryDestination
func (x *ExportEvaluatedExamplesOutputConfig) GetBigqueryDestination() *BigQueryDestinationfunc (*ExportEvaluatedExamplesOutputConfig) GetDestination
func (m *ExportEvaluatedExamplesOutputConfig) GetDestination() isExportEvaluatedExamplesOutputConfig_Destinationfunc (*ExportEvaluatedExamplesOutputConfig) ProtoMessage
func (*ExportEvaluatedExamplesOutputConfig) ProtoMessage()func (*ExportEvaluatedExamplesOutputConfig) ProtoReflect
func (x *ExportEvaluatedExamplesOutputConfig) ProtoReflect() protoreflect.Messagefunc (*ExportEvaluatedExamplesOutputConfig) Reset
func (x *ExportEvaluatedExamplesOutputConfig) Reset()func (*ExportEvaluatedExamplesOutputConfig) String
func (x *ExportEvaluatedExamplesOutputConfig) String() stringExportEvaluatedExamplesOutputConfig_BigqueryDestination
type ExportEvaluatedExamplesOutputConfig_BigqueryDestination struct {
// The BigQuery location where the output is to be written to.
BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}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"`
// contains filtered or unexported fields
}Request message for [AutoMl.ExportEvaluatedExamples][google.cloud.automl.v1beta1.AutoMl.ExportEvaluatedExamples].
func (*ExportEvaluatedExamplesRequest) Descriptor
func (*ExportEvaluatedExamplesRequest) Descriptor() ([]byte, []int)Deprecated: Use ExportEvaluatedExamplesRequest.ProtoReflect.Descriptor instead.
func (*ExportEvaluatedExamplesRequest) GetName
func (x *ExportEvaluatedExamplesRequest) GetName() stringfunc (*ExportEvaluatedExamplesRequest) GetOutputConfig
func (x *ExportEvaluatedExamplesRequest) GetOutputConfig() *ExportEvaluatedExamplesOutputConfigfunc (*ExportEvaluatedExamplesRequest) ProtoMessage
func (*ExportEvaluatedExamplesRequest) ProtoMessage()func (*ExportEvaluatedExamplesRequest) ProtoReflect
func (x *ExportEvaluatedExamplesRequest) ProtoReflect() protoreflect.Messagefunc (*ExportEvaluatedExamplesRequest) Reset
func (x *ExportEvaluatedExamplesRequest) Reset()func (*ExportEvaluatedExamplesRequest) String
func (x *ExportEvaluatedExamplesRequest) String() stringExportModelOperationMetadata
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"`
// contains filtered or unexported fields
}Details of ExportModel operation.
func (*ExportModelOperationMetadata) Descriptor
func (*ExportModelOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use ExportModelOperationMetadata.ProtoReflect.Descriptor instead.
func (*ExportModelOperationMetadata) GetOutputInfo
func (x *ExportModelOperationMetadata) GetOutputInfo() *ExportModelOperationMetadata_ExportModelOutputInfofunc (*ExportModelOperationMetadata) ProtoMessage
func (*ExportModelOperationMetadata) ProtoMessage()func (*ExportModelOperationMetadata) ProtoReflect
func (x *ExportModelOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*ExportModelOperationMetadata) Reset
func (x *ExportModelOperationMetadata) Reset()func (*ExportModelOperationMetadata) String
func (x *ExportModelOperationMetadata) String() stringExportModelOperationMetadata_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"`
// contains filtered or unexported fields
}Further describes the output of model export. Supplements
[ModelExportOutputConfig][google.cloud.automl.v1beta1.ModelExportOutputConfig].
func (*ExportModelOperationMetadata_ExportModelOutputInfo) Descriptor
func (*ExportModelOperationMetadata_ExportModelOutputInfo) Descriptor() ([]byte, []int)Deprecated: Use ExportModelOperationMetadata_ExportModelOutputInfo.ProtoReflect.Descriptor instead.
func (*ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory
func (x *ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory() stringfunc (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage
func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage()func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoReflect
func (x *ExportModelOperationMetadata_ExportModelOutputInfo) ProtoReflect() protoreflect.Messagefunc (*ExportModelOperationMetadata_ExportModelOutputInfo) Reset
func (x *ExportModelOperationMetadata_ExportModelOutputInfo) Reset()func (*ExportModelOperationMetadata_ExportModelOutputInfo) String
func (x *ExportModelOperationMetadata_ExportModelOutputInfo) String() stringExportModelRequest
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"`
// contains filtered or unexported fields
}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)Deprecated: Use ExportModelRequest.ProtoReflect.Descriptor instead.
func (*ExportModelRequest) GetName
func (x *ExportModelRequest) GetName() stringfunc (*ExportModelRequest) GetOutputConfig
func (x *ExportModelRequest) GetOutputConfig() *ModelExportOutputConfigfunc (*ExportModelRequest) ProtoMessage
func (*ExportModelRequest) ProtoMessage()func (*ExportModelRequest) ProtoReflect
func (x *ExportModelRequest) ProtoReflect() protoreflect.Messagefunc (*ExportModelRequest) Reset
func (x *ExportModelRequest) Reset()func (*ExportModelRequest) String
func (x *ExportModelRequest) String() stringFloat64Stats
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"`
// contains filtered or unexported fields
}The data statistics of a series of FLOAT64 values.
func (*Float64Stats) Descriptor
func (*Float64Stats) Descriptor() ([]byte, []int)Deprecated: Use Float64Stats.ProtoReflect.Descriptor instead.
func (*Float64Stats) GetHistogramBuckets
func (x *Float64Stats) GetHistogramBuckets() []*Float64Stats_HistogramBucketfunc (*Float64Stats) GetMean
func (x *Float64Stats) GetMean() float64func (*Float64Stats) GetQuantiles
func (x *Float64Stats) GetQuantiles() []float64func (*Float64Stats) GetStandardDeviation
func (x *Float64Stats) GetStandardDeviation() float64func (*Float64Stats) ProtoMessage
func (*Float64Stats) ProtoMessage()func (*Float64Stats) ProtoReflect
func (x *Float64Stats) ProtoReflect() protoreflect.Messagefunc (*Float64Stats) Reset
func (x *Float64Stats) Reset()func (*Float64Stats) String
func (x *Float64Stats) String() stringFloat64Stats_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"`
// contains filtered or unexported fields
}A bucket of a histogram.
func (*Float64Stats_HistogramBucket) Descriptor
func (*Float64Stats_HistogramBucket) Descriptor() ([]byte, []int)Deprecated: Use Float64Stats_HistogramBucket.ProtoReflect.Descriptor instead.
func (*Float64Stats_HistogramBucket) GetCount
func (x *Float64Stats_HistogramBucket) GetCount() int64func (*Float64Stats_HistogramBucket) GetMax
func (x *Float64Stats_HistogramBucket) GetMax() float64func (*Float64Stats_HistogramBucket) GetMin
func (x *Float64Stats_HistogramBucket) GetMin() float64func (*Float64Stats_HistogramBucket) ProtoMessage
func (*Float64Stats_HistogramBucket) ProtoMessage()func (*Float64Stats_HistogramBucket) ProtoReflect
func (x *Float64Stats_HistogramBucket) ProtoReflect() protoreflect.Messagefunc (*Float64Stats_HistogramBucket) Reset
func (x *Float64Stats_HistogramBucket) Reset()func (*Float64Stats_HistogramBucket) String
func (x *Float64Stats_HistogramBucket) String() stringGcrDestination
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"`
// contains filtered or unexported fields
}The GCR location where the image must be pushed to.
func (*GcrDestination) Descriptor
func (*GcrDestination) Descriptor() ([]byte, []int)Deprecated: Use GcrDestination.ProtoReflect.Descriptor instead.
func (*GcrDestination) GetOutputUri
func (x *GcrDestination) GetOutputUri() stringfunc (*GcrDestination) ProtoMessage
func (*GcrDestination) ProtoMessage()func (*GcrDestination) ProtoReflect
func (x *GcrDestination) ProtoReflect() protoreflect.Messagefunc (*GcrDestination) Reset
func (x *GcrDestination) Reset()func (*GcrDestination) String
func (x *GcrDestination) String() stringGcsDestination
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"`
// contains filtered or unexported fields
}The Google Cloud Storage location where the output is to be written to.
func (*GcsDestination) Descriptor
func (*GcsDestination) Descriptor() ([]byte, []int)Deprecated: Use GcsDestination.ProtoReflect.Descriptor instead.
func (*GcsDestination) GetOutputUriPrefix
func (x *GcsDestination) GetOutputUriPrefix() stringfunc (*GcsDestination) ProtoMessage
func (*GcsDestination) ProtoMessage()func (*GcsDestination) ProtoReflect
func (x *GcsDestination) ProtoReflect() protoreflect.Messagefunc (*GcsDestination) Reset
func (x *GcsDestination) Reset()func (*GcsDestination) String
func (x *GcsDestination) String() stringGcsSource
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"`
// contains filtered or unexported fields
}The Google Cloud Storage location for the input content.
func (*GcsSource) Descriptor
Deprecated: Use GcsSource.ProtoReflect.Descriptor instead.
func (*GcsSource) GetInputUris
func (*GcsSource) ProtoMessage
func (*GcsSource) ProtoMessage()func (*GcsSource) ProtoReflect
func (x *GcsSource) ProtoReflect() protoreflect.Messagefunc (*GcsSource) Reset
func (x *GcsSource) Reset()func (*GcsSource) String
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"`
// contains filtered or unexported fields
}Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1beta1.AutoMl.GetAnnotationSpec].
func (*GetAnnotationSpecRequest) Descriptor
func (*GetAnnotationSpecRequest) Descriptor() ([]byte, []int)Deprecated: Use GetAnnotationSpecRequest.ProtoReflect.Descriptor instead.
func (*GetAnnotationSpecRequest) GetName
func (x *GetAnnotationSpecRequest) GetName() stringfunc (*GetAnnotationSpecRequest) ProtoMessage
func (*GetAnnotationSpecRequest) ProtoMessage()func (*GetAnnotationSpecRequest) ProtoReflect
func (x *GetAnnotationSpecRequest) ProtoReflect() protoreflect.Messagefunc (*GetAnnotationSpecRequest) Reset
func (x *GetAnnotationSpecRequest) Reset()func (*GetAnnotationSpecRequest) String
func (x *GetAnnotationSpecRequest) String() stringGetColumnSpecRequest
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 *fieldmaskpb.FieldMask `protobuf:"bytes,2,opt,name=field_mask,json=fieldMask,proto3" json:"field_mask,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.GetColumnSpec][google.cloud.automl.v1beta1.AutoMl.GetColumnSpec].
func (*GetColumnSpecRequest) Descriptor
func (*GetColumnSpecRequest) Descriptor() ([]byte, []int)Deprecated: Use GetColumnSpecRequest.ProtoReflect.Descriptor instead.
func (*GetColumnSpecRequest) GetFieldMask
func (x *GetColumnSpecRequest) GetFieldMask() *fieldmaskpb.FieldMaskfunc (*GetColumnSpecRequest) GetName
func (x *GetColumnSpecRequest) GetName() stringfunc (*GetColumnSpecRequest) ProtoMessage
func (*GetColumnSpecRequest) ProtoMessage()func (*GetColumnSpecRequest) ProtoReflect
func (x *GetColumnSpecRequest) ProtoReflect() protoreflect.Messagefunc (*GetColumnSpecRequest) Reset
func (x *GetColumnSpecRequest) Reset()func (*GetColumnSpecRequest) String
func (x *GetColumnSpecRequest) String() stringGetDatasetRequest
type GetDatasetRequest struct {
// Required. The resource name of the dataset to retrieve.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.GetDataset][google.cloud.automl.v1beta1.AutoMl.GetDataset].
func (*GetDatasetRequest) Descriptor
func (*GetDatasetRequest) Descriptor() ([]byte, []int)Deprecated: Use GetDatasetRequest.ProtoReflect.Descriptor instead.
func (*GetDatasetRequest) GetName
func (x *GetDatasetRequest) GetName() stringfunc (*GetDatasetRequest) ProtoMessage
func (*GetDatasetRequest) ProtoMessage()func (*GetDatasetRequest) ProtoReflect
func (x *GetDatasetRequest) ProtoReflect() protoreflect.Messagefunc (*GetDatasetRequest) Reset
func (x *GetDatasetRequest) Reset()func (*GetDatasetRequest) String
func (x *GetDatasetRequest) String() stringGetModelEvaluationRequest
type GetModelEvaluationRequest struct {
// Required. Resource name for the model evaluation.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1beta1.AutoMl.GetModelEvaluation].
func (*GetModelEvaluationRequest) Descriptor
func (*GetModelEvaluationRequest) Descriptor() ([]byte, []int)Deprecated: Use GetModelEvaluationRequest.ProtoReflect.Descriptor instead.
func (*GetModelEvaluationRequest) GetName
func (x *GetModelEvaluationRequest) GetName() stringfunc (*GetModelEvaluationRequest) ProtoMessage
func (*GetModelEvaluationRequest) ProtoMessage()func (*GetModelEvaluationRequest) ProtoReflect
func (x *GetModelEvaluationRequest) ProtoReflect() protoreflect.Messagefunc (*GetModelEvaluationRequest) Reset
func (x *GetModelEvaluationRequest) Reset()func (*GetModelEvaluationRequest) String
func (x *GetModelEvaluationRequest) String() stringGetModelRequest
type GetModelRequest struct {
// Required. Resource name of the model.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.GetModel][google.cloud.automl.v1beta1.AutoMl.GetModel].
func (*GetModelRequest) Descriptor
func (*GetModelRequest) Descriptor() ([]byte, []int)Deprecated: Use GetModelRequest.ProtoReflect.Descriptor instead.
func (*GetModelRequest) GetName
func (x *GetModelRequest) GetName() stringfunc (*GetModelRequest) ProtoMessage
func (*GetModelRequest) ProtoMessage()func (*GetModelRequest) ProtoReflect
func (x *GetModelRequest) ProtoReflect() protoreflect.Messagefunc (*GetModelRequest) Reset
func (x *GetModelRequest) Reset()func (*GetModelRequest) String
func (x *GetModelRequest) String() stringGetTableSpecRequest
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 *fieldmaskpb.FieldMask `protobuf:"bytes,2,opt,name=field_mask,json=fieldMask,proto3" json:"field_mask,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.GetTableSpec][google.cloud.automl.v1beta1.AutoMl.GetTableSpec].
func (*GetTableSpecRequest) Descriptor
func (*GetTableSpecRequest) Descriptor() ([]byte, []int)Deprecated: Use GetTableSpecRequest.ProtoReflect.Descriptor instead.
func (*GetTableSpecRequest) GetFieldMask
func (x *GetTableSpecRequest) GetFieldMask() *fieldmaskpb.FieldMaskfunc (*GetTableSpecRequest) GetName
func (x *GetTableSpecRequest) GetName() stringfunc (*GetTableSpecRequest) ProtoMessage
func (*GetTableSpecRequest) ProtoMessage()func (*GetTableSpecRequest) ProtoReflect
func (x *GetTableSpecRequest) ProtoReflect() protoreflect.Messagefunc (*GetTableSpecRequest) Reset
func (x *GetTableSpecRequest) Reset()func (*GetTableSpecRequest) String
func (x *GetTableSpecRequest) String() stringImage
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 assignable 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"`
// contains filtered or unexported fields
}A representation of an image. Only images up to 30MB in size are supported.
func (*Image) Descriptor
Deprecated: Use Image.ProtoReflect.Descriptor instead.
func (*Image) GetData
func (m *Image) GetData() isImage_Datafunc (*Image) GetImageBytes
func (*Image) GetInputConfig
func (x *Image) GetInputConfig() *InputConfigfunc (*Image) GetThumbnailUri
func (*Image) ProtoMessage
func (*Image) ProtoMessage()func (*Image) ProtoReflect
func (x *Image) ProtoReflect() protoreflect.Messagefunc (*Image) Reset
func (x *Image) Reset()func (*Image) String
ImageClassificationDatasetMetadata
type ImageClassificationDatasetMetadata struct {
ClassificationType ClassificationType "" /* 168 byte string literal not displayed */
}Dataset metadata that is specific to image classification.
func (*ImageClassificationDatasetMetadata) Descriptor
func (*ImageClassificationDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use ImageClassificationDatasetMetadata.ProtoReflect.Descriptor instead.
func (*ImageClassificationDatasetMetadata) GetClassificationType
func (x *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationTypefunc (*ImageClassificationDatasetMetadata) ProtoMessage
func (*ImageClassificationDatasetMetadata) ProtoMessage()func (*ImageClassificationDatasetMetadata) ProtoReflect
func (x *ImageClassificationDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*ImageClassificationDatasetMetadata) Reset
func (x *ImageClassificationDatasetMetadata) Reset()func (*ImageClassificationDatasetMetadata) String
func (x *ImageClassificationDatasetMetadata) String() stringImageClassificationModelDeploymentMetadata
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"`
// contains filtered or unexported fields
}Model deployment metadata specific to Image Classification.
func (*ImageClassificationModelDeploymentMetadata) Descriptor
func (*ImageClassificationModelDeploymentMetadata) Descriptor() ([]byte, []int)Deprecated: Use ImageClassificationModelDeploymentMetadata.ProtoReflect.Descriptor instead.
func (*ImageClassificationModelDeploymentMetadata) GetNodeCount
func (x *ImageClassificationModelDeploymentMetadata) GetNodeCount() int64func (*ImageClassificationModelDeploymentMetadata) ProtoMessage
func (*ImageClassificationModelDeploymentMetadata) ProtoMessage()func (*ImageClassificationModelDeploymentMetadata) ProtoReflect
func (x *ImageClassificationModelDeploymentMetadata) ProtoReflect() protoreflect.Messagefunc (*ImageClassificationModelDeploymentMetadata) Reset
func (x *ImageClassificationModelDeploymentMetadata) Reset()func (*ImageClassificationModelDeploymentMetadata) String
func (x *ImageClassificationModelDeploymentMetadata) String() stringImageClassificationModelMetadata
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"`
// contains filtered or unexported fields
}Model metadata for image classification.
func (*ImageClassificationModelMetadata) Descriptor
func (*ImageClassificationModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use ImageClassificationModelMetadata.ProtoReflect.Descriptor instead.
func (*ImageClassificationModelMetadata) GetBaseModelId
func (x *ImageClassificationModelMetadata) GetBaseModelId() stringfunc (*ImageClassificationModelMetadata) GetModelType
func (x *ImageClassificationModelMetadata) GetModelType() stringfunc (*ImageClassificationModelMetadata) GetNodeCount
func (x *ImageClassificationModelMetadata) GetNodeCount() int64func (*ImageClassificationModelMetadata) GetNodeQps
func (x *ImageClassificationModelMetadata) GetNodeQps() float64func (*ImageClassificationModelMetadata) GetStopReason
func (x *ImageClassificationModelMetadata) GetStopReason() stringfunc (*ImageClassificationModelMetadata) GetTrainBudget
func (x *ImageClassificationModelMetadata) GetTrainBudget() int64func (*ImageClassificationModelMetadata) GetTrainCost
func (x *ImageClassificationModelMetadata) GetTrainCost() int64func (*ImageClassificationModelMetadata) ProtoMessage
func (*ImageClassificationModelMetadata) ProtoMessage()func (*ImageClassificationModelMetadata) ProtoReflect
func (x *ImageClassificationModelMetadata) ProtoReflect() protoreflect.Messagefunc (*ImageClassificationModelMetadata) Reset
func (x *ImageClassificationModelMetadata) Reset()func (*ImageClassificationModelMetadata) String
func (x *ImageClassificationModelMetadata) String() stringImageObjectDetectionAnnotation
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"`
// contains filtered or unexported fields
}Annotation details for image object detection.
func (*ImageObjectDetectionAnnotation) Descriptor
func (*ImageObjectDetectionAnnotation) Descriptor() ([]byte, []int)Deprecated: Use ImageObjectDetectionAnnotation.ProtoReflect.Descriptor instead.
func (*ImageObjectDetectionAnnotation) GetBoundingBox
func (x *ImageObjectDetectionAnnotation) GetBoundingBox() *BoundingPolyfunc (*ImageObjectDetectionAnnotation) GetScore
func (x *ImageObjectDetectionAnnotation) GetScore() float32func (*ImageObjectDetectionAnnotation) ProtoMessage
func (*ImageObjectDetectionAnnotation) ProtoMessage()func (*ImageObjectDetectionAnnotation) ProtoReflect
func (x *ImageObjectDetectionAnnotation) ProtoReflect() protoreflect.Messagefunc (*ImageObjectDetectionAnnotation) Reset
func (x *ImageObjectDetectionAnnotation) Reset()func (*ImageObjectDetectionAnnotation) String
func (x *ImageObjectDetectionAnnotation) String() stringImageObjectDetectionDatasetMetadata
type ImageObjectDetectionDatasetMetadata struct {
// contains filtered or unexported fields
}Dataset metadata specific to image object detection.
func (*ImageObjectDetectionDatasetMetadata) Descriptor
func (*ImageObjectDetectionDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use ImageObjectDetectionDatasetMetadata.ProtoReflect.Descriptor instead.
func (*ImageObjectDetectionDatasetMetadata) ProtoMessage
func (*ImageObjectDetectionDatasetMetadata) ProtoMessage()func (*ImageObjectDetectionDatasetMetadata) ProtoReflect
func (x *ImageObjectDetectionDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*ImageObjectDetectionDatasetMetadata) Reset
func (x *ImageObjectDetectionDatasetMetadata) Reset()func (*ImageObjectDetectionDatasetMetadata) String
func (x *ImageObjectDetectionDatasetMetadata) String() stringImageObjectDetectionEvaluationMetrics
type ImageObjectDetectionEvaluationMetrics struct {
EvaluatedBoundingBoxCount int32 "" /* 141 byte string literal not displayed */
BoundingBoxMetricsEntries []*BoundingBoxMetricsEntry "" /* 140 byte string literal not displayed */
BoundingBoxMeanAveragePrecision float32 "" /* 162 byte string literal not displayed */
}Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
func (*ImageObjectDetectionEvaluationMetrics) Descriptor
func (*ImageObjectDetectionEvaluationMetrics) Descriptor() ([]byte, []int)Deprecated: Use ImageObjectDetectionEvaluationMetrics.ProtoReflect.Descriptor instead.
func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision
func (x *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision() float32func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries
func (x *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries() []*BoundingBoxMetricsEntryfunc (*ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount
func (x *ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount() int32func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage
func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage()func (*ImageObjectDetectionEvaluationMetrics) ProtoReflect
func (x *ImageObjectDetectionEvaluationMetrics) ProtoReflect() protoreflect.Messagefunc (*ImageObjectDetectionEvaluationMetrics) Reset
func (x *ImageObjectDetectionEvaluationMetrics) Reset()func (*ImageObjectDetectionEvaluationMetrics) String
func (x *ImageObjectDetectionEvaluationMetrics) String() stringImageObjectDetectionModelDeploymentMetadata
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"`
// contains filtered or unexported fields
}Model deployment metadata specific to Image Object Detection.
func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor
func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor() ([]byte, []int)Deprecated: Use ImageObjectDetectionModelDeploymentMetadata.ProtoReflect.Descriptor instead.
func (*ImageObjectDetectionModelDeploymentMetadata) GetNodeCount
func (x *ImageObjectDetectionModelDeploymentMetadata) GetNodeCount() int64func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage
func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage()func (*ImageObjectDetectionModelDeploymentMetadata) ProtoReflect
func (x *ImageObjectDetectionModelDeploymentMetadata) ProtoReflect() protoreflect.Messagefunc (*ImageObjectDetectionModelDeploymentMetadata) Reset
func (x *ImageObjectDetectionModelDeploymentMetadata) Reset()func (*ImageObjectDetectionModelDeploymentMetadata) String
func (x *ImageObjectDetectionModelDeploymentMetadata) String() stringImageObjectDetectionModelMetadata
type ImageObjectDetectionModelMetadata struct {
ModelType string `protobuf:"bytes,1,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`
NodeCount int64 `protobuf:"varint,3,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
NodeQps float64 `protobuf:"fixed64,4,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`
StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`
TrainBudgetMilliNodeHours int64 "" /* 143 byte string literal not displayed */
TrainCostMilliNodeHours int64 "" /* 137 byte string literal not displayed */
}Model metadata specific to image object detection.
func (*ImageObjectDetectionModelMetadata) Descriptor
func (*ImageObjectDetectionModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use ImageObjectDetectionModelMetadata.ProtoReflect.Descriptor instead.
func (*ImageObjectDetectionModelMetadata) GetModelType
func (x *ImageObjectDetectionModelMetadata) GetModelType() stringfunc (*ImageObjectDetectionModelMetadata) GetNodeCount
func (x *ImageObjectDetectionModelMetadata) GetNodeCount() int64func (*ImageObjectDetectionModelMetadata) GetNodeQps
func (x *ImageObjectDetectionModelMetadata) GetNodeQps() float64func (*ImageObjectDetectionModelMetadata) GetStopReason
func (x *ImageObjectDetectionModelMetadata) GetStopReason() stringfunc (*ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours
func (x *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64func (*ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours
func (x *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64func (*ImageObjectDetectionModelMetadata) ProtoMessage
func (*ImageObjectDetectionModelMetadata) ProtoMessage()func (*ImageObjectDetectionModelMetadata) ProtoReflect
func (x *ImageObjectDetectionModelMetadata) ProtoReflect() protoreflect.Messagefunc (*ImageObjectDetectionModelMetadata) Reset
func (x *ImageObjectDetectionModelMetadata) Reset()func (*ImageObjectDetectionModelMetadata) String
func (x *ImageObjectDetectionModelMetadata) String() stringImage_ImageBytes
type Image_ImageBytes struct {
// Image content represented as a stream of bytes.
// Note: As with all `bytes` fields, protobuffers use a pure binary
// representation, whereas JSON representations use base64.
ImageBytes []byte `protobuf:"bytes,1,opt,name=image_bytes,json=imageBytes,proto3,oneof"`
}Image_InputConfig
type Image_InputConfig struct {
// An input config specifying the content of the image.
InputConfig *InputConfig `protobuf:"bytes,6,opt,name=input_config,json=inputConfig,proto3,oneof"`
}ImportDataOperationMetadata
type ImportDataOperationMetadata struct {
// contains filtered or unexported fields
}Details of ImportData operation.
func (*ImportDataOperationMetadata) Descriptor
func (*ImportDataOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use ImportDataOperationMetadata.ProtoReflect.Descriptor instead.
func (*ImportDataOperationMetadata) ProtoMessage
func (*ImportDataOperationMetadata) ProtoMessage()func (*ImportDataOperationMetadata) ProtoReflect
func (x *ImportDataOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*ImportDataOperationMetadata) Reset
func (x *ImportDataOperationMetadata) Reset()func (*ImportDataOperationMetadata) String
func (x *ImportDataOperationMetadata) String() stringImportDataRequest
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"`
// contains filtered or unexported fields
}Request message for [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData].
func (*ImportDataRequest) Descriptor
func (*ImportDataRequest) Descriptor() ([]byte, []int)Deprecated: Use ImportDataRequest.ProtoReflect.Descriptor instead.
func (*ImportDataRequest) GetInputConfig
func (x *ImportDataRequest) GetInputConfig() *InputConfigfunc (*ImportDataRequest) GetName
func (x *ImportDataRequest) GetName() stringfunc (*ImportDataRequest) ProtoMessage
func (*ImportDataRequest) ProtoMessage()func (*ImportDataRequest) ProtoReflect
func (x *ImportDataRequest) ProtoReflect() protoreflect.Messagefunc (*ImportDataRequest) Reset
func (x *ImportDataRequest) Reset()func (*ImportDataRequest) String
func (x *ImportDataRequest) String() stringInputConfig
type InputConfig struct {
Source isInputConfig_Source `protobuf_oneof:"source"`
Params map[string]string "" /* 153 byte string literal not displayed */
}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)Deprecated: Use InputConfig.ProtoReflect.Descriptor instead.
func (*InputConfig) GetBigquerySource
func (x *InputConfig) GetBigquerySource() *BigQuerySourcefunc (*InputConfig) GetGcsSource
func (x *InputConfig) GetGcsSource() *GcsSourcefunc (*InputConfig) GetParams
func (x *InputConfig) GetParams() map[string]stringfunc (*InputConfig) GetSource
func (m *InputConfig) GetSource() isInputConfig_Sourcefunc (*InputConfig) ProtoMessage
func (*InputConfig) ProtoMessage()func (*InputConfig) ProtoReflect
func (x *InputConfig) ProtoReflect() protoreflect.Messagefunc (*InputConfig) Reset
func (x *InputConfig) Reset()func (*InputConfig) String
func (x *InputConfig) String() stringInputConfig_BigquerySource
type InputConfig_BigquerySource struct {
// The BigQuery location for the input content.
BigquerySource *BigQuerySource `protobuf:"bytes,3,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}InputConfig_GcsSource
type InputConfig_GcsSource struct {
// The Google Cloud Storage location for the input content.
// In ImportData, the gcs_source points to a csv with structure described in
// the comment.
GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}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 *fieldmaskpb.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"`
// contains filtered or unexported fields
}Request message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].
func (*ListColumnSpecsRequest) Descriptor
func (*ListColumnSpecsRequest) Descriptor() ([]byte, []int)Deprecated: Use ListColumnSpecsRequest.ProtoReflect.Descriptor instead.
func (*ListColumnSpecsRequest) GetFieldMask
func (x *ListColumnSpecsRequest) GetFieldMask() *fieldmaskpb.FieldMaskfunc (*ListColumnSpecsRequest) GetFilter
func (x *ListColumnSpecsRequest) GetFilter() stringfunc (*ListColumnSpecsRequest) GetPageSize
func (x *ListColumnSpecsRequest) GetPageSize() int32func (*ListColumnSpecsRequest) GetPageToken
func (x *ListColumnSpecsRequest) GetPageToken() stringfunc (*ListColumnSpecsRequest) GetParent
func (x *ListColumnSpecsRequest) GetParent() stringfunc (*ListColumnSpecsRequest) ProtoMessage
func (*ListColumnSpecsRequest) ProtoMessage()func (*ListColumnSpecsRequest) ProtoReflect
func (x *ListColumnSpecsRequest) ProtoReflect() protoreflect.Messagefunc (*ListColumnSpecsRequest) Reset
func (x *ListColumnSpecsRequest) Reset()func (*ListColumnSpecsRequest) String
func (x *ListColumnSpecsRequest) String() stringListColumnSpecsResponse
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"`
// contains filtered or unexported fields
}Response message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].
func (*ListColumnSpecsResponse) Descriptor
func (*ListColumnSpecsResponse) Descriptor() ([]byte, []int)Deprecated: Use ListColumnSpecsResponse.ProtoReflect.Descriptor instead.
func (*ListColumnSpecsResponse) GetColumnSpecs
func (x *ListColumnSpecsResponse) GetColumnSpecs() []*ColumnSpecfunc (*ListColumnSpecsResponse) GetNextPageToken
func (x *ListColumnSpecsResponse) GetNextPageToken() stringfunc (*ListColumnSpecsResponse) ProtoMessage
func (*ListColumnSpecsResponse) ProtoMessage()func (*ListColumnSpecsResponse) ProtoReflect
func (x *ListColumnSpecsResponse) ProtoReflect() protoreflect.Messagefunc (*ListColumnSpecsResponse) Reset
func (x *ListColumnSpecsResponse) Reset()func (*ListColumnSpecsResponse) String
func (x *ListColumnSpecsResponse) String() stringListDatasetsRequest
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"`
// contains filtered or unexported fields
}Request message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].
func (*ListDatasetsRequest) Descriptor
func (*ListDatasetsRequest) Descriptor() ([]byte, []int)Deprecated: Use ListDatasetsRequest.ProtoReflect.Descriptor instead.
func (*ListDatasetsRequest) GetFilter
func (x *ListDatasetsRequest) GetFilter() stringfunc (*ListDatasetsRequest) GetPageSize
func (x *ListDatasetsRequest) GetPageSize() int32func (*ListDatasetsRequest) GetPageToken
func (x *ListDatasetsRequest) GetPageToken() stringfunc (*ListDatasetsRequest) GetParent
func (x *ListDatasetsRequest) GetParent() stringfunc (*ListDatasetsRequest) ProtoMessage
func (*ListDatasetsRequest) ProtoMessage()func (*ListDatasetsRequest) ProtoReflect
func (x *ListDatasetsRequest) ProtoReflect() protoreflect.Messagefunc (*ListDatasetsRequest) Reset
func (x *ListDatasetsRequest) Reset()func (*ListDatasetsRequest) String
func (x *ListDatasetsRequest) String() stringListDatasetsResponse
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"`
// contains filtered or unexported fields
}Response message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].
func (*ListDatasetsResponse) Descriptor
func (*ListDatasetsResponse) Descriptor() ([]byte, []int)Deprecated: Use ListDatasetsResponse.ProtoReflect.Descriptor instead.
func (*ListDatasetsResponse) GetDatasets
func (x *ListDatasetsResponse) GetDatasets() []*Datasetfunc (*ListDatasetsResponse) GetNextPageToken
func (x *ListDatasetsResponse) GetNextPageToken() stringfunc (*ListDatasetsResponse) ProtoMessage
func (*ListDatasetsResponse) ProtoMessage()func (*ListDatasetsResponse) ProtoReflect
func (x *ListDatasetsResponse) ProtoReflect() protoreflect.Messagefunc (*ListDatasetsResponse) Reset
func (x *ListDatasetsResponse) Reset()func (*ListDatasetsResponse) String
func (x *ListDatasetsResponse) String() stringListModelEvaluationsRequest
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"`
// contains filtered or unexported fields
}Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].
func (*ListModelEvaluationsRequest) Descriptor
func (*ListModelEvaluationsRequest) Descriptor() ([]byte, []int)Deprecated: Use ListModelEvaluationsRequest.ProtoReflect.Descriptor instead.
func (*ListModelEvaluationsRequest) GetFilter
func (x *ListModelEvaluationsRequest) GetFilter() stringfunc (*ListModelEvaluationsRequest) GetPageSize
func (x *ListModelEvaluationsRequest) GetPageSize() int32func (*ListModelEvaluationsRequest) GetPageToken
func (x *ListModelEvaluationsRequest) GetPageToken() stringfunc (*ListModelEvaluationsRequest) GetParent
func (x *ListModelEvaluationsRequest) GetParent() stringfunc (*ListModelEvaluationsRequest) ProtoMessage
func (*ListModelEvaluationsRequest) ProtoMessage()func (*ListModelEvaluationsRequest) ProtoReflect
func (x *ListModelEvaluationsRequest) ProtoReflect() protoreflect.Messagefunc (*ListModelEvaluationsRequest) Reset
func (x *ListModelEvaluationsRequest) Reset()func (*ListModelEvaluationsRequest) String
func (x *ListModelEvaluationsRequest) String() stringListModelEvaluationsResponse
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"`
// contains filtered or unexported fields
}Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].
func (*ListModelEvaluationsResponse) Descriptor
func (*ListModelEvaluationsResponse) Descriptor() ([]byte, []int)Deprecated: Use ListModelEvaluationsResponse.ProtoReflect.Descriptor instead.
func (*ListModelEvaluationsResponse) GetModelEvaluation
func (x *ListModelEvaluationsResponse) GetModelEvaluation() []*ModelEvaluationfunc (*ListModelEvaluationsResponse) GetNextPageToken
func (x *ListModelEvaluationsResponse) GetNextPageToken() stringfunc (*ListModelEvaluationsResponse) ProtoMessage
func (*ListModelEvaluationsResponse) ProtoMessage()func (*ListModelEvaluationsResponse) ProtoReflect
func (x *ListModelEvaluationsResponse) ProtoReflect() protoreflect.Messagefunc (*ListModelEvaluationsResponse) Reset
func (x *ListModelEvaluationsResponse) Reset()func (*ListModelEvaluationsResponse) String
func (x *ListModelEvaluationsResponse) String() stringListModelsRequest
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"`
// contains filtered or unexported fields
}Request message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].
func (*ListModelsRequest) Descriptor
func (*ListModelsRequest) Descriptor() ([]byte, []int)Deprecated: Use ListModelsRequest.ProtoReflect.Descriptor instead.
func (*ListModelsRequest) GetFilter
func (x *ListModelsRequest) GetFilter() stringfunc (*ListModelsRequest) GetPageSize
func (x *ListModelsRequest) GetPageSize() int32func (*ListModelsRequest) GetPageToken
func (x *ListModelsRequest) GetPageToken() stringfunc (*ListModelsRequest) GetParent
func (x *ListModelsRequest) GetParent() stringfunc (*ListModelsRequest) ProtoMessage
func (*ListModelsRequest) ProtoMessage()func (*ListModelsRequest) ProtoReflect
func (x *ListModelsRequest) ProtoReflect() protoreflect.Messagefunc (*ListModelsRequest) Reset
func (x *ListModelsRequest) Reset()func (*ListModelsRequest) String
func (x *ListModelsRequest) String() stringListModelsResponse
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"`
// contains filtered or unexported fields
}Response message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].
func (*ListModelsResponse) Descriptor
func (*ListModelsResponse) Descriptor() ([]byte, []int)Deprecated: Use ListModelsResponse.ProtoReflect.Descriptor instead.
func (*ListModelsResponse) GetModel
func (x *ListModelsResponse) GetModel() []*Modelfunc (*ListModelsResponse) GetNextPageToken
func (x *ListModelsResponse) GetNextPageToken() stringfunc (*ListModelsResponse) ProtoMessage
func (*ListModelsResponse) ProtoMessage()func (*ListModelsResponse) ProtoReflect
func (x *ListModelsResponse) ProtoReflect() protoreflect.Messagefunc (*ListModelsResponse) Reset
func (x *ListModelsResponse) Reset()func (*ListModelsResponse) String
func (x *ListModelsResponse) String() stringListTableSpecsRequest
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 *fieldmaskpb.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"`
// contains filtered or unexported fields
}Request message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].
func (*ListTableSpecsRequest) Descriptor
func (*ListTableSpecsRequest) Descriptor() ([]byte, []int)Deprecated: Use ListTableSpecsRequest.ProtoReflect.Descriptor instead.
func (*ListTableSpecsRequest) GetFieldMask
func (x *ListTableSpecsRequest) GetFieldMask() *fieldmaskpb.FieldMaskfunc (*ListTableSpecsRequest) GetFilter
func (x *ListTableSpecsRequest) GetFilter() stringfunc (*ListTableSpecsRequest) GetPageSize
func (x *ListTableSpecsRequest) GetPageSize() int32func (*ListTableSpecsRequest) GetPageToken
func (x *ListTableSpecsRequest) GetPageToken() stringfunc (*ListTableSpecsRequest) GetParent
func (x *ListTableSpecsRequest) GetParent() stringfunc (*ListTableSpecsRequest) ProtoMessage
func (*ListTableSpecsRequest) ProtoMessage()func (*ListTableSpecsRequest) ProtoReflect
func (x *ListTableSpecsRequest) ProtoReflect() protoreflect.Messagefunc (*ListTableSpecsRequest) Reset
func (x *ListTableSpecsRequest) Reset()func (*ListTableSpecsRequest) String
func (x *ListTableSpecsRequest) String() stringListTableSpecsResponse
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"`
// contains filtered or unexported fields
}Response message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].
func (*ListTableSpecsResponse) Descriptor
func (*ListTableSpecsResponse) Descriptor() ([]byte, []int)Deprecated: Use ListTableSpecsResponse.ProtoReflect.Descriptor instead.
func (*ListTableSpecsResponse) GetNextPageToken
func (x *ListTableSpecsResponse) GetNextPageToken() stringfunc (*ListTableSpecsResponse) GetTableSpecs
func (x *ListTableSpecsResponse) GetTableSpecs() []*TableSpecfunc (*ListTableSpecsResponse) ProtoMessage
func (*ListTableSpecsResponse) ProtoMessage()func (*ListTableSpecsResponse) ProtoReflect
func (x *ListTableSpecsResponse) ProtoReflect() protoreflect.Messagefunc (*ListTableSpecsResponse) Reset
func (x *ListTableSpecsResponse) Reset()func (*ListTableSpecsResponse) String
func (x *ListTableSpecsResponse) String() stringModel
type Model struct {
ModelMetadata isModel_ModelMetadata `protobuf_oneof:"model_metadata"`
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
DatasetId string `protobuf:"bytes,3,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
CreateTime *timestamppb.Timestamp `protobuf:"bytes,7,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
UpdateTime *timestamppb.Timestamp `protobuf:"bytes,11,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
DeploymentState Model_DeploymentState "" /* 162 byte string literal not displayed */
}API proto representing a trained machine learning model.
func (*Model) Descriptor
Deprecated: Use Model.ProtoReflect.Descriptor instead.
func (*Model) GetCreateTime
func (x *Model) GetCreateTime() *timestamppb.Timestampfunc (*Model) GetDatasetId
func (*Model) GetDeploymentState
func (x *Model) GetDeploymentState() Model_DeploymentStatefunc (*Model) GetDisplayName
func (*Model) GetImageClassificationModelMetadata
func (x *Model) GetImageClassificationModelMetadata() *ImageClassificationModelMetadatafunc (*Model) GetImageObjectDetectionModelMetadata
func (x *Model) GetImageObjectDetectionModelMetadata() *ImageObjectDetectionModelMetadatafunc (*Model) GetModelMetadata
func (m *Model) GetModelMetadata() isModel_ModelMetadatafunc (*Model) GetName
func (*Model) GetTablesModelMetadata
func (x *Model) GetTablesModelMetadata() *TablesModelMetadatafunc (*Model) GetTextClassificationModelMetadata
func (x *Model) GetTextClassificationModelMetadata() *TextClassificationModelMetadatafunc (*Model) GetTextExtractionModelMetadata
func (x *Model) GetTextExtractionModelMetadata() *TextExtractionModelMetadatafunc (*Model) GetTextSentimentModelMetadata
func (x *Model) GetTextSentimentModelMetadata() *TextSentimentModelMetadatafunc (*Model) GetTranslationModelMetadata
func (x *Model) GetTranslationModelMetadata() *TranslationModelMetadatafunc (*Model) GetUpdateTime
func (x *Model) GetUpdateTime() *timestamppb.Timestampfunc (*Model) GetVideoClassificationModelMetadata
func (x *Model) GetVideoClassificationModelMetadata() *VideoClassificationModelMetadatafunc (*Model) GetVideoObjectTrackingModelMetadata
func (x *Model) GetVideoObjectTrackingModelMetadata() *VideoObjectTrackingModelMetadatafunc (*Model) ProtoMessage
func (*Model) ProtoMessage()func (*Model) ProtoReflect
func (x *Model) ProtoReflect() protoreflect.Messagefunc (*Model) Reset
func (x *Model) Reset()func (*Model) String
ModelEvaluation
type ModelEvaluation struct {
Metrics isModelEvaluation_Metrics `protobuf_oneof:"metrics"`
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
AnnotationSpecId string `protobuf:"bytes,2,opt,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
DisplayName string `protobuf:"bytes,15,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
CreateTime *timestamppb.Timestamp `protobuf:"bytes,5,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
EvaluatedExampleCount int32 "" /* 127 byte string literal not displayed */
}Evaluation results of a model.
func (*ModelEvaluation) Descriptor
func (*ModelEvaluation) Descriptor() ([]byte, []int)Deprecated: Use ModelEvaluation.ProtoReflect.Descriptor instead.
func (*ModelEvaluation) GetAnnotationSpecId
func (x *ModelEvaluation) GetAnnotationSpecId() stringfunc (*ModelEvaluation) GetClassificationEvaluationMetrics
func (x *ModelEvaluation) GetClassificationEvaluationMetrics() *ClassificationEvaluationMetricsfunc (*ModelEvaluation) GetCreateTime
func (x *ModelEvaluation) GetCreateTime() *timestamppb.Timestampfunc (*ModelEvaluation) GetDisplayName
func (x *ModelEvaluation) GetDisplayName() stringfunc (*ModelEvaluation) GetEvaluatedExampleCount
func (x *ModelEvaluation) GetEvaluatedExampleCount() int32func (*ModelEvaluation) GetImageObjectDetectionEvaluationMetrics
func (x *ModelEvaluation) GetImageObjectDetectionEvaluationMetrics() *ImageObjectDetectionEvaluationMetricsfunc (*ModelEvaluation) GetMetrics
func (m *ModelEvaluation) GetMetrics() isModelEvaluation_Metricsfunc (*ModelEvaluation) GetName
func (x *ModelEvaluation) GetName() stringfunc (*ModelEvaluation) GetRegressionEvaluationMetrics
func (x *ModelEvaluation) GetRegressionEvaluationMetrics() *RegressionEvaluationMetricsfunc (*ModelEvaluation) GetTextExtractionEvaluationMetrics
func (x *ModelEvaluation) GetTextExtractionEvaluationMetrics() *TextExtractionEvaluationMetricsfunc (*ModelEvaluation) GetTextSentimentEvaluationMetrics
func (x *ModelEvaluation) GetTextSentimentEvaluationMetrics() *TextSentimentEvaluationMetricsfunc (*ModelEvaluation) GetTranslationEvaluationMetrics
func (x *ModelEvaluation) GetTranslationEvaluationMetrics() *TranslationEvaluationMetricsfunc (*ModelEvaluation) GetVideoObjectTrackingEvaluationMetrics
func (x *ModelEvaluation) GetVideoObjectTrackingEvaluationMetrics() *VideoObjectTrackingEvaluationMetricsfunc (*ModelEvaluation) ProtoMessage
func (*ModelEvaluation) ProtoMessage()func (*ModelEvaluation) ProtoReflect
func (x *ModelEvaluation) ProtoReflect() protoreflect.Messagefunc (*ModelEvaluation) Reset
func (x *ModelEvaluation) Reset()func (*ModelEvaluation) String
func (x *ModelEvaluation) String() stringModelEvaluation_ClassificationEvaluationMetrics
type ModelEvaluation_ClassificationEvaluationMetrics struct {
// Model evaluation metrics for image, text, video and tables
// classification.
// Tables problem is considered a classification when the target column
// is CATEGORY DataType.
ClassificationEvaluationMetrics *ClassificationEvaluationMetrics `protobuf:"bytes,8,opt,name=classification_evaluation_metrics,json=classificationEvaluationMetrics,proto3,oneof"`
}ModelEvaluation_ImageObjectDetectionEvaluationMetrics
type ModelEvaluation_ImageObjectDetectionEvaluationMetrics struct {
// Model evaluation metrics for image object detection.
ImageObjectDetectionEvaluationMetrics *ImageObjectDetectionEvaluationMetrics `protobuf:"bytes,12,opt,name=image_object_detection_evaluation_metrics,json=imageObjectDetectionEvaluationMetrics,proto3,oneof"`
}ModelEvaluation_RegressionEvaluationMetrics
type ModelEvaluation_RegressionEvaluationMetrics struct {
// Model evaluation metrics for Tables regression.
// Tables problem is considered a regression when the target column
// has FLOAT64 DataType.
RegressionEvaluationMetrics *RegressionEvaluationMetrics `protobuf:"bytes,24,opt,name=regression_evaluation_metrics,json=regressionEvaluationMetrics,proto3,oneof"`
}ModelEvaluation_TextExtractionEvaluationMetrics
type ModelEvaluation_TextExtractionEvaluationMetrics struct {
// Evaluation metrics for text extraction models.
TextExtractionEvaluationMetrics *TextExtractionEvaluationMetrics `protobuf:"bytes,13,opt,name=text_extraction_evaluation_metrics,json=textExtractionEvaluationMetrics,proto3,oneof"`
}ModelEvaluation_TextSentimentEvaluationMetrics
type ModelEvaluation_TextSentimentEvaluationMetrics struct {
// Evaluation metrics for text sentiment models.
TextSentimentEvaluationMetrics *TextSentimentEvaluationMetrics `protobuf:"bytes,11,opt,name=text_sentiment_evaluation_metrics,json=textSentimentEvaluationMetrics,proto3,oneof"`
}ModelEvaluation_TranslationEvaluationMetrics
type ModelEvaluation_TranslationEvaluationMetrics struct {
// Model evaluation metrics for translation.
TranslationEvaluationMetrics *TranslationEvaluationMetrics `protobuf:"bytes,9,opt,name=translation_evaluation_metrics,json=translationEvaluationMetrics,proto3,oneof"`
}ModelEvaluation_VideoObjectTrackingEvaluationMetrics
type ModelEvaluation_VideoObjectTrackingEvaluationMetrics struct {
// Model evaluation metrics for video object tracking.
VideoObjectTrackingEvaluationMetrics *VideoObjectTrackingEvaluationMetrics `protobuf:"bytes,14,opt,name=video_object_tracking_evaluation_metrics,json=videoObjectTrackingEvaluationMetrics,proto3,oneof"`
}ModelExportOutputConfig
type ModelExportOutputConfig struct {
Destination isModelExportOutputConfig_Destination `protobuf_oneof:"destination"`
ModelFormat string `protobuf:"bytes,4,opt,name=model_format,json=modelFormat,proto3" json:"model_format,omitempty"`
Params map[string]string "" /* 153 byte string literal not displayed */
}Output configuration for ModelExport Action.
func (*ModelExportOutputConfig) Descriptor
func (*ModelExportOutputConfig) Descriptor() ([]byte, []int)Deprecated: Use ModelExportOutputConfig.ProtoReflect.Descriptor instead.
func (*ModelExportOutputConfig) GetDestination
func (m *ModelExportOutputConfig) GetDestination() isModelExportOutputConfig_Destinationfunc (*ModelExportOutputConfig) GetGcrDestination
func (x *ModelExportOutputConfig) GetGcrDestination() *GcrDestinationfunc (*ModelExportOutputConfig) GetGcsDestination
func (x *ModelExportOutputConfig) GetGcsDestination() *GcsDestinationfunc (*ModelExportOutputConfig) GetModelFormat
func (x *ModelExportOutputConfig) GetModelFormat() stringfunc (*ModelExportOutputConfig) GetParams
func (x *ModelExportOutputConfig) GetParams() map[string]stringfunc (*ModelExportOutputConfig) ProtoMessage
func (*ModelExportOutputConfig) ProtoMessage()func (*ModelExportOutputConfig) ProtoReflect
func (x *ModelExportOutputConfig) ProtoReflect() protoreflect.Messagefunc (*ModelExportOutputConfig) Reset
func (x *ModelExportOutputConfig) Reset()func (*ModelExportOutputConfig) String
func (x *ModelExportOutputConfig) String() stringModelExportOutputConfig_GcrDestination
type ModelExportOutputConfig_GcrDestination struct {
// The GCR location where model image is to be pushed to. This location
// may only be set for the following model formats:
// "docker".
//
// The model image will be created under the given URI.
GcrDestination *GcrDestination `protobuf:"bytes,3,opt,name=gcr_destination,json=gcrDestination,proto3,oneof"`
}ModelExportOutputConfig_GcsDestination
type ModelExportOutputConfig_GcsDestination struct {
// The Google Cloud Storage location where the model is to be written to.
// This location may only be set for the following model formats:
// "tflite", "edgetpu_tflite", "tf_saved_model", "tf_js", "core_ml".
//
// Under the directory given as the destination a new one with name
// "model-export-Model_DeploymentState
type Model_DeploymentState int32Deployment state of the model.
Model_DEPLOYMENT_STATE_UNSPECIFIED, Model_DEPLOYED, Model_UNDEPLOYED
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) Descriptor
func (Model_DeploymentState) Descriptor() protoreflect.EnumDescriptorfunc (Model_DeploymentState) Enum
func (x Model_DeploymentState) Enum() *Model_DeploymentStatefunc (Model_DeploymentState) EnumDescriptor
func (Model_DeploymentState) EnumDescriptor() ([]byte, []int)Deprecated: Use Model_DeploymentState.Descriptor instead.
func (Model_DeploymentState) Number
func (x Model_DeploymentState) Number() protoreflect.EnumNumberfunc (Model_DeploymentState) String
func (x Model_DeploymentState) String() stringfunc (Model_DeploymentState) Type
func (Model_DeploymentState) Type() protoreflect.EnumTypeModel_ImageClassificationModelMetadata
type Model_ImageClassificationModelMetadata struct {
// Metadata for image classification models.
ImageClassificationModelMetadata *ImageClassificationModelMetadata `protobuf:"bytes,13,opt,name=image_classification_model_metadata,json=imageClassificationModelMetadata,proto3,oneof"`
}Model_ImageObjectDetectionModelMetadata
type Model_ImageObjectDetectionModelMetadata struct {
// Metadata for image object detection models.
ImageObjectDetectionModelMetadata *ImageObjectDetectionModelMetadata `protobuf:"bytes,20,opt,name=image_object_detection_model_metadata,json=imageObjectDetectionModelMetadata,proto3,oneof"`
}Model_TablesModelMetadata
type Model_TablesModelMetadata struct {
// Metadata for Tables models.
TablesModelMetadata *TablesModelMetadata `protobuf:"bytes,24,opt,name=tables_model_metadata,json=tablesModelMetadata,proto3,oneof"`
}Model_TextClassificationModelMetadata
type Model_TextClassificationModelMetadata struct {
// Metadata for text classification models.
TextClassificationModelMetadata *TextClassificationModelMetadata `protobuf:"bytes,14,opt,name=text_classification_model_metadata,json=textClassificationModelMetadata,proto3,oneof"`
}Model_TextExtractionModelMetadata
type Model_TextExtractionModelMetadata struct {
// Metadata for text extraction models.
TextExtractionModelMetadata *TextExtractionModelMetadata `protobuf:"bytes,19,opt,name=text_extraction_model_metadata,json=textExtractionModelMetadata,proto3,oneof"`
}Model_TextSentimentModelMetadata
type Model_TextSentimentModelMetadata struct {
// Metadata for text sentiment models.
TextSentimentModelMetadata *TextSentimentModelMetadata `protobuf:"bytes,22,opt,name=text_sentiment_model_metadata,json=textSentimentModelMetadata,proto3,oneof"`
}Model_TranslationModelMetadata
type Model_TranslationModelMetadata struct {
// Metadata for translation models.
TranslationModelMetadata *TranslationModelMetadata `protobuf:"bytes,15,opt,name=translation_model_metadata,json=translationModelMetadata,proto3,oneof"`
}Model_VideoClassificationModelMetadata
type Model_VideoClassificationModelMetadata struct {
// Metadata for video classification models.
VideoClassificationModelMetadata *VideoClassificationModelMetadata `protobuf:"bytes,23,opt,name=video_classification_model_metadata,json=videoClassificationModelMetadata,proto3,oneof"`
}Model_VideoObjectTrackingModelMetadata
type Model_VideoObjectTrackingModelMetadata struct {
// Metadata for video object tracking models.
VideoObjectTrackingModelMetadata *VideoObjectTrackingModelMetadata `protobuf:"bytes,21,opt,name=video_object_tracking_model_metadata,json=videoObjectTrackingModelMetadata,proto3,oneof"`
}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"`
// contains filtered or unexported fields
}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)Deprecated: Use NormalizedVertex.ProtoReflect.Descriptor instead.
func (*NormalizedVertex) GetX
func (x *NormalizedVertex) GetX() float32func (*NormalizedVertex) GetY
func (x *NormalizedVertex) GetY() float32func (*NormalizedVertex) ProtoMessage
func (*NormalizedVertex) ProtoMessage()func (*NormalizedVertex) ProtoReflect
func (x *NormalizedVertex) ProtoReflect() protoreflect.Messagefunc (*NormalizedVertex) Reset
func (x *NormalizedVertex) Reset()func (*NormalizedVertex) String
func (x *NormalizedVertex) String() stringOperationMetadata
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 assignable 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 *timestamppb.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 *timestamppb.Timestamp `protobuf:"bytes,4,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
// contains filtered or unexported fields
}Metadata used across all long running operations returned by AutoML API.
func (*OperationMetadata) Descriptor
func (*OperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use OperationMetadata.ProtoReflect.Descriptor instead.
func (*OperationMetadata) GetBatchPredictDetails
func (x *OperationMetadata) GetBatchPredictDetails() *BatchPredictOperationMetadatafunc (*OperationMetadata) GetCreateModelDetails
func (x *OperationMetadata) GetCreateModelDetails() *CreateModelOperationMetadatafunc (*OperationMetadata) GetCreateTime
func (x *OperationMetadata) GetCreateTime() *timestamppb.Timestampfunc (*OperationMetadata) GetDeleteDetails
func (x *OperationMetadata) GetDeleteDetails() *DeleteOperationMetadatafunc (*OperationMetadata) GetDeployModelDetails
func (x *OperationMetadata) GetDeployModelDetails() *DeployModelOperationMetadatafunc (*OperationMetadata) GetDetails
func (m *OperationMetadata) GetDetails() isOperationMetadata_Detailsfunc (*OperationMetadata) GetExportDataDetails
func (x *OperationMetadata) GetExportDataDetails() *ExportDataOperationMetadatafunc (*OperationMetadata) GetExportEvaluatedExamplesDetails
func (x *OperationMetadata) GetExportEvaluatedExamplesDetails() *ExportEvaluatedExamplesOperationMetadatafunc (*OperationMetadata) GetExportModelDetails
func (x *OperationMetadata) GetExportModelDetails() *ExportModelOperationMetadatafunc (*OperationMetadata) GetImportDataDetails
func (x *OperationMetadata) GetImportDataDetails() *ImportDataOperationMetadatafunc (*OperationMetadata) GetPartialFailures
func (x *OperationMetadata) GetPartialFailures() []*status.Statusfunc (*OperationMetadata) GetProgressPercent
func (x *OperationMetadata) GetProgressPercent() int32func (*OperationMetadata) GetUndeployModelDetails
func (x *OperationMetadata) GetUndeployModelDetails() *UndeployModelOperationMetadatafunc (*OperationMetadata) GetUpdateTime
func (x *OperationMetadata) GetUpdateTime() *timestamppb.Timestampfunc (*OperationMetadata) ProtoMessage
func (*OperationMetadata) ProtoMessage()func (*OperationMetadata) ProtoReflect
func (x *OperationMetadata) ProtoReflect() protoreflect.Messagefunc (*OperationMetadata) Reset
func (x *OperationMetadata) Reset()func (*OperationMetadata) String
func (x *OperationMetadata) String() stringOperationMetadata_BatchPredictDetails
type OperationMetadata_BatchPredictDetails struct {
// Details of BatchPredict operation.
BatchPredictDetails *BatchPredictOperationMetadata `protobuf:"bytes,16,opt,name=batch_predict_details,json=batchPredictDetails,proto3,oneof"`
}OperationMetadata_CreateModelDetails
type OperationMetadata_CreateModelDetails struct {
// Details of CreateModel operation.
CreateModelDetails *CreateModelOperationMetadata `protobuf:"bytes,10,opt,name=create_model_details,json=createModelDetails,proto3,oneof"`
}OperationMetadata_DeleteDetails
type OperationMetadata_DeleteDetails struct {
// Details of a Delete operation.
DeleteDetails *DeleteOperationMetadata `protobuf:"bytes,8,opt,name=delete_details,json=deleteDetails,proto3,oneof"`
}OperationMetadata_DeployModelDetails
type OperationMetadata_DeployModelDetails struct {
// Details of a DeployModel operation.
DeployModelDetails *DeployModelOperationMetadata `protobuf:"bytes,24,opt,name=deploy_model_details,json=deployModelDetails,proto3,oneof"`
}OperationMetadata_ExportDataDetails
type OperationMetadata_ExportDataDetails struct {
// Details of ExportData operation.
ExportDataDetails *ExportDataOperationMetadata `protobuf:"bytes,21,opt,name=export_data_details,json=exportDataDetails,proto3,oneof"`
}OperationMetadata_ExportEvaluatedExamplesDetails
type OperationMetadata_ExportEvaluatedExamplesDetails struct {
// Details of ExportEvaluatedExamples operation.
ExportEvaluatedExamplesDetails *ExportEvaluatedExamplesOperationMetadata `protobuf:"bytes,26,opt,name=export_evaluated_examples_details,json=exportEvaluatedExamplesDetails,proto3,oneof"`
}OperationMetadata_ExportModelDetails
type OperationMetadata_ExportModelDetails struct {
// Details of ExportModel operation.
ExportModelDetails *ExportModelOperationMetadata `protobuf:"bytes,22,opt,name=export_model_details,json=exportModelDetails,proto3,oneof"`
}OperationMetadata_ImportDataDetails
type OperationMetadata_ImportDataDetails struct {
// Details of ImportData operation.
ImportDataDetails *ImportDataOperationMetadata `protobuf:"bytes,15,opt,name=import_data_details,json=importDataDetails,proto3,oneof"`
}OperationMetadata_UndeployModelDetails
type OperationMetadata_UndeployModelDetails struct {
// Details of an UndeployModel operation.
UndeployModelDetails *UndeployModelOperationMetadata `protobuf:"bytes,25,opt,name=undeploy_model_details,json=undeployModelDetails,proto3,oneof"`
}OutputConfig
type OutputConfig struct {
// Required. The destination of the output.
//
// Types that are assignable to Destination:
// *OutputConfig_GcsDestination
// *OutputConfig_BigqueryDestination
Destination isOutputConfig_Destination `protobuf_oneof:"destination"`
// contains filtered or unexported fields
}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)Deprecated: Use OutputConfig.ProtoReflect.Descriptor instead.
func (*OutputConfig) GetBigqueryDestination
func (x *OutputConfig) GetBigqueryDestination() *BigQueryDestinationfunc (*OutputConfig) GetDestination
func (m *OutputConfig) GetDestination() isOutputConfig_Destinationfunc (*OutputConfig) GetGcsDestination
func (x *OutputConfig) GetGcsDestination() *GcsDestinationfunc (*OutputConfig) ProtoMessage
func (*OutputConfig) ProtoMessage()func (*OutputConfig) ProtoReflect
func (x *OutputConfig) ProtoReflect() protoreflect.Messagefunc (*OutputConfig) Reset
func (x *OutputConfig) Reset()func (*OutputConfig) String
func (x *OutputConfig) String() stringOutputConfig_BigqueryDestination
type OutputConfig_BigqueryDestination struct {
// The BigQuery location where the output is to be written to.
BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}OutputConfig_GcsDestination
type OutputConfig_GcsDestination struct {
// The Google Cloud Storage location where the output is to be written to.
// For Image Object Detection, Text Extraction, Video Classification and
// Tables, in the given directory a new directory will be created with name:
// export_data-PredictRequest
type PredictRequest struct {
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
Payload *ExamplePayload `protobuf:"bytes,2,opt,name=payload,proto3" json:"payload,omitempty"`
Params map[string]string "" /* 153 byte string literal not displayed */
}Request message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].
func (*PredictRequest) Descriptor
func (*PredictRequest) Descriptor() ([]byte, []int)Deprecated: Use PredictRequest.ProtoReflect.Descriptor instead.
func (*PredictRequest) GetName
func (x *PredictRequest) GetName() stringfunc (*PredictRequest) GetParams
func (x *PredictRequest) GetParams() map[string]stringfunc (*PredictRequest) GetPayload
func (x *PredictRequest) GetPayload() *ExamplePayloadfunc (*PredictRequest) ProtoMessage
func (*PredictRequest) ProtoMessage()func (*PredictRequest) ProtoReflect
func (x *PredictRequest) ProtoReflect() protoreflect.Messagefunc (*PredictRequest) Reset
func (x *PredictRequest) Reset()func (*PredictRequest) String
func (x *PredictRequest) String() stringPredictResponse
type PredictResponse struct {
Payload []*AnnotationPayload `protobuf:"bytes,1,rep,name=payload,proto3" json:"payload,omitempty"`
PreprocessedInput *ExamplePayload `protobuf:"bytes,3,opt,name=preprocessed_input,json=preprocessedInput,proto3" json:"preprocessed_input,omitempty"`
Metadata map[string]string "" /* 157 byte string literal not displayed */
}Response message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].
func (*PredictResponse) Descriptor
func (*PredictResponse) Descriptor() ([]byte, []int)Deprecated: Use PredictResponse.ProtoReflect.Descriptor instead.
func (*PredictResponse) GetMetadata
func (x *PredictResponse) GetMetadata() map[string]stringfunc (*PredictResponse) GetPayload
func (x *PredictResponse) GetPayload() []*AnnotationPayloadfunc (*PredictResponse) GetPreprocessedInput
func (x *PredictResponse) GetPreprocessedInput() *ExamplePayloadfunc (*PredictResponse) ProtoMessage
func (*PredictResponse) ProtoMessage()func (*PredictResponse) ProtoReflect
func (x *PredictResponse) ProtoReflect() protoreflect.Messagefunc (*PredictResponse) Reset
func (x *PredictResponse) Reset()func (*PredictResponse) String
func (x *PredictResponse) String() stringPredictionServiceClient
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) (*longrunningpb.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) PredictionServiceClientPredictionServiceServer
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) (*longrunningpb.Operation, error)
}PredictionServiceServer is the server API for PredictionService service.
RegressionEvaluationMetrics
type RegressionEvaluationMetrics struct {
RootMeanSquaredError float32 "" /* 127 byte string literal not displayed */
MeanAbsoluteError float32 `protobuf:"fixed32,2,opt,name=mean_absolute_error,json=meanAbsoluteError,proto3" json:"mean_absolute_error,omitempty"`
MeanAbsolutePercentageError float32 "" /* 148 byte string literal not displayed */
RSquared float32 `protobuf:"fixed32,4,opt,name=r_squared,json=rSquared,proto3" json:"r_squared,omitempty"`
RootMeanSquaredLogError float32 "" /* 138 byte string literal not displayed */
}Metrics for regression problems.
func (*RegressionEvaluationMetrics) Descriptor
func (*RegressionEvaluationMetrics) Descriptor() ([]byte, []int)Deprecated: Use RegressionEvaluationMetrics.ProtoReflect.Descriptor instead.
func (*RegressionEvaluationMetrics) GetMeanAbsoluteError
func (x *RegressionEvaluationMetrics) GetMeanAbsoluteError() float32func (*RegressionEvaluationMetrics) GetMeanAbsolutePercentageError
func (x *RegressionEvaluationMetrics) GetMeanAbsolutePercentageError() float32func (*RegressionEvaluationMetrics) GetRSquared
func (x *RegressionEvaluationMetrics) GetRSquared() float32func (*RegressionEvaluationMetrics) GetRootMeanSquaredError
func (x *RegressionEvaluationMetrics) GetRootMeanSquaredError() float32func (*RegressionEvaluationMetrics) GetRootMeanSquaredLogError
func (x *RegressionEvaluationMetrics) GetRootMeanSquaredLogError() float32func (*RegressionEvaluationMetrics) ProtoMessage
func (*RegressionEvaluationMetrics) ProtoMessage()func (*RegressionEvaluationMetrics) ProtoReflect
func (x *RegressionEvaluationMetrics) ProtoReflect() protoreflect.Messagefunc (*RegressionEvaluationMetrics) Reset
func (x *RegressionEvaluationMetrics) Reset()func (*RegressionEvaluationMetrics) String
func (x *RegressionEvaluationMetrics) String() stringRow
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 []*structpb.Value `protobuf:"bytes,3,rep,name=values,proto3" json:"values,omitempty"`
// contains filtered or unexported fields
}A representation of a row in a relational table.
func (*Row) Descriptor
Deprecated: Use Row.ProtoReflect.Descriptor instead.
func (*Row) GetColumnSpecIds
func (*Row) GetValues
func (*Row) ProtoMessage
func (*Row) ProtoMessage()func (*Row) ProtoReflect
func (x *Row) ProtoReflect() protoreflect.Messagefunc (*Row) Reset
func (x *Row) Reset()func (*Row) String
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"`
// contains filtered or unexported fields
}The data statistics of a series of STRING values.
func (*StringStats) Descriptor
func (*StringStats) Descriptor() ([]byte, []int)Deprecated: Use StringStats.ProtoReflect.Descriptor instead.
func (*StringStats) GetTopUnigramStats
func (x *StringStats) GetTopUnigramStats() []*StringStats_UnigramStatsfunc (*StringStats) ProtoMessage
func (*StringStats) ProtoMessage()func (*StringStats) ProtoReflect
func (x *StringStats) ProtoReflect() protoreflect.Messagefunc (*StringStats) Reset
func (x *StringStats) Reset()func (*StringStats) String
func (x *StringStats) String() stringStringStats_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"`
// contains filtered or unexported fields
}The statistics of a unigram.
func (*StringStats_UnigramStats) Descriptor
func (*StringStats_UnigramStats) Descriptor() ([]byte, []int)Deprecated: Use StringStats_UnigramStats.ProtoReflect.Descriptor instead.
func (*StringStats_UnigramStats) GetCount
func (x *StringStats_UnigramStats) GetCount() int64func (*StringStats_UnigramStats) GetValue
func (x *StringStats_UnigramStats) GetValue() stringfunc (*StringStats_UnigramStats) ProtoMessage
func (*StringStats_UnigramStats) ProtoMessage()func (*StringStats_UnigramStats) ProtoReflect
func (x *StringStats_UnigramStats) ProtoReflect() protoreflect.Messagefunc (*StringStats_UnigramStats) Reset
func (x *StringStats_UnigramStats) Reset()func (*StringStats_UnigramStats) String
func (x *StringStats_UnigramStats) String() stringStructStats
type StructStats struct {
FieldStats map[string]*DataStats "" /* 179 byte string literal not displayed */
}The data statistics of a series of STRUCT values.
func (*StructStats) Descriptor
func (*StructStats) Descriptor() ([]byte, []int)Deprecated: Use StructStats.ProtoReflect.Descriptor instead.
func (*StructStats) GetFieldStats
func (x *StructStats) GetFieldStats() map[string]*DataStatsfunc (*StructStats) ProtoMessage
func (*StructStats) ProtoMessage()func (*StructStats) ProtoReflect
func (x *StructStats) ProtoReflect() protoreflect.Messagefunc (*StructStats) Reset
func (x *StructStats) Reset()func (*StructStats) String
func (x *StructStats) String() stringStructType
type StructType struct {
Fields map[string]*DataType "" /* 153 byte string literal not displayed */
}StructType defines the DataType-s of a [STRUCT][google.cloud.automl.v1beta1.TypeCode.STRUCT] type.
func (*StructType) Descriptor
func (*StructType) Descriptor() ([]byte, []int)Deprecated: Use StructType.ProtoReflect.Descriptor instead.
func (*StructType) GetFields
func (x *StructType) GetFields() map[string]*DataTypefunc (*StructType) ProtoMessage
func (*StructType) ProtoMessage()func (*StructType) ProtoReflect
func (x *StructType) ProtoReflect() protoreflect.Messagefunc (*StructType) Reset
func (x *StructType) Reset()func (*StructType) String
func (x *StructType) String() stringTableSpec
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"`
// contains filtered or unexported fields
}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
Deprecated: Use TableSpec.ProtoReflect.Descriptor instead.
func (*TableSpec) GetColumnCount
func (*TableSpec) GetEtag
func (*TableSpec) GetInputConfigs
func (x *TableSpec) GetInputConfigs() []*InputConfigfunc (*TableSpec) GetName
func (*TableSpec) GetRowCount
func (*TableSpec) GetTimeColumnSpecId
func (*TableSpec) GetValidRowCount
func (*TableSpec) ProtoMessage
func (*TableSpec) ProtoMessage()func (*TableSpec) ProtoReflect
func (x *TableSpec) ProtoReflect() protoreflect.Messagefunc (*TableSpec) Reset
func (x *TableSpec) Reset()func (*TableSpec) String
TablesAnnotation
type TablesAnnotation struct {
Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
PredictionInterval *DoubleRange `protobuf:"bytes,4,opt,name=prediction_interval,json=predictionInterval,proto3" json:"prediction_interval,omitempty"`
Value *structpb.Value `protobuf:"bytes,2,opt,name=value,proto3" json:"value,omitempty"`
TablesModelColumnInfo []*TablesModelColumnInfo "" /* 128 byte string literal not displayed */
BaselineScore float32 `protobuf:"fixed32,5,opt,name=baseline_score,json=baselineScore,proto3" json:"baseline_score,omitempty"`
}Contains annotation details specific to Tables.
func (*TablesAnnotation) Descriptor
func (*TablesAnnotation) Descriptor() ([]byte, []int)Deprecated: Use TablesAnnotation.ProtoReflect.Descriptor instead.
func (*TablesAnnotation) GetBaselineScore
func (x *TablesAnnotation) GetBaselineScore() float32func (*TablesAnnotation) GetPredictionInterval
func (x *TablesAnnotation) GetPredictionInterval() *DoubleRangefunc (*TablesAnnotation) GetScore
func (x *TablesAnnotation) GetScore() float32func (*TablesAnnotation) GetTablesModelColumnInfo
func (x *TablesAnnotation) GetTablesModelColumnInfo() []*TablesModelColumnInfofunc (*TablesAnnotation) GetValue
func (x *TablesAnnotation) GetValue() *structpb.Valuefunc (*TablesAnnotation) ProtoMessage
func (*TablesAnnotation) ProtoMessage()func (*TablesAnnotation) ProtoReflect
func (x *TablesAnnotation) ProtoReflect() protoreflect.Messagefunc (*TablesAnnotation) Reset
func (x *TablesAnnotation) Reset()func (*TablesAnnotation) String
func (x *TablesAnnotation) String() stringTablesDatasetMetadata
type TablesDatasetMetadata struct {
PrimaryTableSpecId string `protobuf:"bytes,1,opt,name=primary_table_spec_id,json=primaryTableSpecId,proto3" json:"primary_table_spec_id,omitempty"`
TargetColumnSpecId string `protobuf:"bytes,2,opt,name=target_column_spec_id,json=targetColumnSpecId,proto3" json:"target_column_spec_id,omitempty"`
WeightColumnSpecId string `protobuf:"bytes,3,opt,name=weight_column_spec_id,json=weightColumnSpecId,proto3" json:"weight_column_spec_id,omitempty"`
MlUseColumnSpecId string `protobuf:"bytes,4,opt,name=ml_use_column_spec_id,json=mlUseColumnSpecId,proto3" json:"ml_use_column_spec_id,omitempty"`
TargetColumnCorrelations map[string]*CorrelationStats "" /* 223 byte string literal not displayed */
StatsUpdateTime *timestamppb.Timestamp `protobuf:"bytes,7,opt,name=stats_update_time,json=statsUpdateTime,proto3" json:"stats_update_time,omitempty"`
}Metadata for a dataset used for AutoML Tables.
func (*TablesDatasetMetadata) Descriptor
func (*TablesDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use TablesDatasetMetadata.ProtoReflect.Descriptor instead.
func (*TablesDatasetMetadata) GetMlUseColumnSpecId
func (x *TablesDatasetMetadata) GetMlUseColumnSpecId() stringfunc (*TablesDatasetMetadata) GetPrimaryTableSpecId
func (x *TablesDatasetMetadata) GetPrimaryTableSpecId() stringfunc (*TablesDatasetMetadata) GetStatsUpdateTime
func (x *TablesDatasetMetadata) GetStatsUpdateTime() *timestamppb.Timestampfunc (*TablesDatasetMetadata) GetTargetColumnCorrelations
func (x *TablesDatasetMetadata) GetTargetColumnCorrelations() map[string]*CorrelationStatsfunc (*TablesDatasetMetadata) GetTargetColumnSpecId
func (x *TablesDatasetMetadata) GetTargetColumnSpecId() stringfunc (*TablesDatasetMetadata) GetWeightColumnSpecId
func (x *TablesDatasetMetadata) GetWeightColumnSpecId() stringfunc (*TablesDatasetMetadata) ProtoMessage
func (*TablesDatasetMetadata) ProtoMessage()func (*TablesDatasetMetadata) ProtoReflect
func (x *TablesDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*TablesDatasetMetadata) Reset
func (x *TablesDatasetMetadata) Reset()func (*TablesDatasetMetadata) String
func (x *TablesDatasetMetadata) String() stringTablesModelColumnInfo
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"`
// contains filtered or unexported fields
}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)Deprecated: Use TablesModelColumnInfo.ProtoReflect.Descriptor instead.
func (*TablesModelColumnInfo) GetColumnDisplayName
func (x *TablesModelColumnInfo) GetColumnDisplayName() stringfunc (*TablesModelColumnInfo) GetColumnSpecName
func (x *TablesModelColumnInfo) GetColumnSpecName() stringfunc (*TablesModelColumnInfo) GetFeatureImportance
func (x *TablesModelColumnInfo) GetFeatureImportance() float32func (*TablesModelColumnInfo) ProtoMessage
func (*TablesModelColumnInfo) ProtoMessage()func (*TablesModelColumnInfo) ProtoReflect
func (x *TablesModelColumnInfo) ProtoReflect() protoreflect.Messagefunc (*TablesModelColumnInfo) Reset
func (x *TablesModelColumnInfo) Reset()func (*TablesModelColumnInfo) String
func (x *TablesModelColumnInfo) String() stringTablesModelMetadata
type TablesModelMetadata struct {
AdditionalOptimizationObjectiveConfig isTablesModelMetadata_AdditionalOptimizationObjectiveConfig `protobuf_oneof:"additional_optimization_objective_config"`
TargetColumnSpec *ColumnSpec `protobuf:"bytes,2,opt,name=target_column_spec,json=targetColumnSpec,proto3" json:"target_column_spec,omitempty"`
InputFeatureColumnSpecs []*ColumnSpec "" /* 134 byte string literal not displayed */
OptimizationObjective string `protobuf:"bytes,4,opt,name=optimization_objective,json=optimizationObjective,proto3" json:"optimization_objective,omitempty"`
TablesModelColumnInfo []*TablesModelColumnInfo "" /* 128 byte string literal not displayed */
TrainBudgetMilliNodeHours int64 "" /* 143 byte string literal not displayed */
TrainCostMilliNodeHours int64 "" /* 137 byte string literal not displayed */
DisableEarlyStopping bool `protobuf:"varint,12,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
}Model metadata specific to AutoML Tables.
func (*TablesModelMetadata) Descriptor
func (*TablesModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use TablesModelMetadata.ProtoReflect.Descriptor instead.
func (*TablesModelMetadata) GetAdditionalOptimizationObjectiveConfig
func (m *TablesModelMetadata) GetAdditionalOptimizationObjectiveConfig() isTablesModelMetadata_AdditionalOptimizationObjectiveConfigfunc (*TablesModelMetadata) GetDisableEarlyStopping
func (x *TablesModelMetadata) GetDisableEarlyStopping() boolfunc (*TablesModelMetadata) GetInputFeatureColumnSpecs
func (x *TablesModelMetadata) GetInputFeatureColumnSpecs() []*ColumnSpecfunc (*TablesModelMetadata) GetOptimizationObjective
func (x *TablesModelMetadata) GetOptimizationObjective() stringfunc (*TablesModelMetadata) GetOptimizationObjectivePrecisionValue
func (x *TablesModelMetadata) GetOptimizationObjectivePrecisionValue() float32func (*TablesModelMetadata) GetOptimizationObjectiveRecallValue
func (x *TablesModelMetadata) GetOptimizationObjectiveRecallValue() float32func (*TablesModelMetadata) GetTablesModelColumnInfo
func (x *TablesModelMetadata) GetTablesModelColumnInfo() []*TablesModelColumnInfofunc (*TablesModelMetadata) GetTargetColumnSpec
func (x *TablesModelMetadata) GetTargetColumnSpec() *ColumnSpecfunc (*TablesModelMetadata) GetTrainBudgetMilliNodeHours
func (x *TablesModelMetadata) GetTrainBudgetMilliNodeHours() int64func (*TablesModelMetadata) GetTrainCostMilliNodeHours
func (x *TablesModelMetadata) GetTrainCostMilliNodeHours() int64func (*TablesModelMetadata) ProtoMessage
func (*TablesModelMetadata) ProtoMessage()func (*TablesModelMetadata) ProtoReflect
func (x *TablesModelMetadata) ProtoReflect() protoreflect.Messagefunc (*TablesModelMetadata) Reset
func (x *TablesModelMetadata) Reset()func (*TablesModelMetadata) String
func (x *TablesModelMetadata) String() stringTablesModelMetadata_OptimizationObjectivePrecisionValue
type TablesModelMetadata_OptimizationObjectivePrecisionValue struct {
// Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
// Must be between 0 and 1, inclusive.
OptimizationObjectivePrecisionValue float32 `protobuf:"fixed32,18,opt,name=optimization_objective_precision_value,json=optimizationObjectivePrecisionValue,proto3,oneof"`
}TablesModelMetadata_OptimizationObjectiveRecallValue
type TablesModelMetadata_OptimizationObjectiveRecallValue struct {
// Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
// Must be between 0 and 1, inclusive.
OptimizationObjectiveRecallValue float32 `protobuf:"fixed32,17,opt,name=optimization_objective_recall_value,json=optimizationObjectiveRecallValue,proto3,oneof"`
}TextClassificationDatasetMetadata
type TextClassificationDatasetMetadata struct {
ClassificationType ClassificationType "" /* 168 byte string literal not displayed */
}Dataset metadata for classification.
func (*TextClassificationDatasetMetadata) Descriptor
func (*TextClassificationDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use TextClassificationDatasetMetadata.ProtoReflect.Descriptor instead.
func (*TextClassificationDatasetMetadata) GetClassificationType
func (x *TextClassificationDatasetMetadata) GetClassificationType() ClassificationTypefunc (*TextClassificationDatasetMetadata) ProtoMessage
func (*TextClassificationDatasetMetadata) ProtoMessage()func (*TextClassificationDatasetMetadata) ProtoReflect
func (x *TextClassificationDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*TextClassificationDatasetMetadata) Reset
func (x *TextClassificationDatasetMetadata) Reset()func (*TextClassificationDatasetMetadata) String
func (x *TextClassificationDatasetMetadata) String() stringTextClassificationModelMetadata
type TextClassificationModelMetadata struct {
ClassificationType ClassificationType "" /* 168 byte string literal not displayed */
}Model metadata that is specific to text classification.
func (*TextClassificationModelMetadata) Descriptor
func (*TextClassificationModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use TextClassificationModelMetadata.ProtoReflect.Descriptor instead.
func (*TextClassificationModelMetadata) GetClassificationType
func (x *TextClassificationModelMetadata) GetClassificationType() ClassificationTypefunc (*TextClassificationModelMetadata) ProtoMessage
func (*TextClassificationModelMetadata) ProtoMessage()func (*TextClassificationModelMetadata) ProtoReflect
func (x *TextClassificationModelMetadata) ProtoReflect() protoreflect.Messagefunc (*TextClassificationModelMetadata) Reset
func (x *TextClassificationModelMetadata) Reset()func (*TextClassificationModelMetadata) String
func (x *TextClassificationModelMetadata) String() stringTextExtractionAnnotation
type TextExtractionAnnotation struct {
// Required. Text extraction annotations can either be a text segment or a
// text relation.
//
// Types that are assignable 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"`
// contains filtered or unexported fields
}Annotation for identifying spans of text.
func (*TextExtractionAnnotation) Descriptor
func (*TextExtractionAnnotation) Descriptor() ([]byte, []int)Deprecated: Use TextExtractionAnnotation.ProtoReflect.Descriptor instead.
func (*TextExtractionAnnotation) GetAnnotation
func (m *TextExtractionAnnotation) GetAnnotation() isTextExtractionAnnotation_Annotationfunc (*TextExtractionAnnotation) GetScore
func (x *TextExtractionAnnotation) GetScore() float32func (*TextExtractionAnnotation) GetTextSegment
func (x *TextExtractionAnnotation) GetTextSegment() *TextSegmentfunc (*TextExtractionAnnotation) ProtoMessage
func (*TextExtractionAnnotation) ProtoMessage()func (*TextExtractionAnnotation) ProtoReflect
func (x *TextExtractionAnnotation) ProtoReflect() protoreflect.Messagefunc (*TextExtractionAnnotation) Reset
func (x *TextExtractionAnnotation) Reset()func (*TextExtractionAnnotation) String
func (x *TextExtractionAnnotation) String() stringTextExtractionAnnotation_TextSegment
type TextExtractionAnnotation_TextSegment struct {
// An entity annotation will set this, which is the part of the original
// text to which the annotation pertains.
TextSegment *TextSegment `protobuf:"bytes,3,opt,name=text_segment,json=textSegment,proto3,oneof"`
}TextExtractionDatasetMetadata
type TextExtractionDatasetMetadata struct {
// contains filtered or unexported fields
}Dataset metadata that is specific to text extraction
func (*TextExtractionDatasetMetadata) Descriptor
func (*TextExtractionDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use TextExtractionDatasetMetadata.ProtoReflect.Descriptor instead.
func (*TextExtractionDatasetMetadata) ProtoMessage
func (*TextExtractionDatasetMetadata) ProtoMessage()func (*TextExtractionDatasetMetadata) ProtoReflect
func (x *TextExtractionDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*TextExtractionDatasetMetadata) Reset
func (x *TextExtractionDatasetMetadata) Reset()func (*TextExtractionDatasetMetadata) String
func (x *TextExtractionDatasetMetadata) String() stringTextExtractionEvaluationMetrics
type TextExtractionEvaluationMetrics struct {
AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`
ConfidenceMetricsEntries []*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry "" /* 135 byte string literal not displayed */
}Model evaluation metrics for text extraction problems.
func (*TextExtractionEvaluationMetrics) Descriptor
func (*TextExtractionEvaluationMetrics) Descriptor() ([]byte, []int)Deprecated: Use TextExtractionEvaluationMetrics.ProtoReflect.Descriptor instead.
func (*TextExtractionEvaluationMetrics) GetAuPrc
func (x *TextExtractionEvaluationMetrics) GetAuPrc() float32func (*TextExtractionEvaluationMetrics) GetConfidenceMetricsEntries
func (x *TextExtractionEvaluationMetrics) GetConfidenceMetricsEntries() []*TextExtractionEvaluationMetrics_ConfidenceMetricsEntryfunc (*TextExtractionEvaluationMetrics) ProtoMessage
func (*TextExtractionEvaluationMetrics) ProtoMessage()func (*TextExtractionEvaluationMetrics) ProtoReflect
func (x *TextExtractionEvaluationMetrics) ProtoReflect() protoreflect.Messagefunc (*TextExtractionEvaluationMetrics) Reset
func (x *TextExtractionEvaluationMetrics) Reset()func (*TextExtractionEvaluationMetrics) String
func (x *TextExtractionEvaluationMetrics) String() stringTextExtractionEvaluationMetrics_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"`
// contains filtered or unexported fields
}Metrics for a single confidence threshold.
func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Descriptor
func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)Deprecated: Use TextExtractionEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.
func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold
func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score
func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision
func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetRecall
func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage
func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect
func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Messagefunc (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Reset
func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Reset()func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) String
func (x *TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) String() stringTextExtractionModelMetadata
type TextExtractionModelMetadata struct {
// Indicates the scope of model use case.
//
// * `default`: Use to train a general text extraction model. Default value.
//
// * `health_care`: Use to train a text extraction model that is tuned for
// healthcare applications.
ModelHint string `protobuf:"bytes,3,opt,name=model_hint,json=modelHint,proto3" json:"model_hint,omitempty"`
// contains filtered or unexported fields
}Model metadata that is specific to text extraction.
func (*TextExtractionModelMetadata) Descriptor
func (*TextExtractionModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use TextExtractionModelMetadata.ProtoReflect.Descriptor instead.
func (*TextExtractionModelMetadata) GetModelHint
func (x *TextExtractionModelMetadata) GetModelHint() stringfunc (*TextExtractionModelMetadata) ProtoMessage
func (*TextExtractionModelMetadata) ProtoMessage()func (*TextExtractionModelMetadata) ProtoReflect
func (x *TextExtractionModelMetadata) ProtoReflect() protoreflect.Messagefunc (*TextExtractionModelMetadata) Reset
func (x *TextExtractionModelMetadata) Reset()func (*TextExtractionModelMetadata) String
func (x *TextExtractionModelMetadata) String() stringTextSegment
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"`
// contains filtered or unexported fields
}A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
func (*TextSegment) Descriptor
func (*TextSegment) Descriptor() ([]byte, []int)Deprecated: Use TextSegment.ProtoReflect.Descriptor instead.
func (*TextSegment) GetContent
func (x *TextSegment) GetContent() stringfunc (*TextSegment) GetEndOffset
func (x *TextSegment) GetEndOffset() int64func (*TextSegment) GetStartOffset
func (x *TextSegment) GetStartOffset() int64func (*TextSegment) ProtoMessage
func (*TextSegment) ProtoMessage()func (*TextSegment) ProtoReflect
func (x *TextSegment) ProtoReflect() protoreflect.Messagefunc (*TextSegment) Reset
func (x *TextSegment) Reset()func (*TextSegment) String
func (x *TextSegment) String() stringTextSentimentAnnotation
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"`
// contains filtered or unexported fields
}Contains annotation details specific to text sentiment.
func (*TextSentimentAnnotation) Descriptor
func (*TextSentimentAnnotation) Descriptor() ([]byte, []int)Deprecated: Use TextSentimentAnnotation.ProtoReflect.Descriptor instead.
func (*TextSentimentAnnotation) GetSentiment
func (x *TextSentimentAnnotation) GetSentiment() int32func (*TextSentimentAnnotation) ProtoMessage
func (*TextSentimentAnnotation) ProtoMessage()func (*TextSentimentAnnotation) ProtoReflect
func (x *TextSentimentAnnotation) ProtoReflect() protoreflect.Messagefunc (*TextSentimentAnnotation) Reset
func (x *TextSentimentAnnotation) Reset()func (*TextSentimentAnnotation) String
func (x *TextSentimentAnnotation) String() stringTextSentimentDatasetMetadata
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"`
// contains filtered or unexported fields
}Dataset metadata for text sentiment.
func (*TextSentimentDatasetMetadata) Descriptor
func (*TextSentimentDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use TextSentimentDatasetMetadata.ProtoReflect.Descriptor instead.
func (*TextSentimentDatasetMetadata) GetSentimentMax
func (x *TextSentimentDatasetMetadata) GetSentimentMax() int32func (*TextSentimentDatasetMetadata) ProtoMessage
func (*TextSentimentDatasetMetadata) ProtoMessage()func (*TextSentimentDatasetMetadata) ProtoReflect
func (x *TextSentimentDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*TextSentimentDatasetMetadata) Reset
func (x *TextSentimentDatasetMetadata) Reset()func (*TextSentimentDatasetMetadata) String
func (x *TextSentimentDatasetMetadata) String() stringTextSentimentEvaluationMetrics
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 .
//
// Deprecated: Marked as deprecated in google/cloud/automl/v1beta1/text_sentiment.proto.
AnnotationSpecId []string `protobuf:"bytes,9,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
// contains filtered or unexported fields
}Model evaluation metrics for text sentiment problems.
func (*TextSentimentEvaluationMetrics) Descriptor
func (*TextSentimentEvaluationMetrics) Descriptor() ([]byte, []int)Deprecated: Use TextSentimentEvaluationMetrics.ProtoReflect.Descriptor instead.
func (*TextSentimentEvaluationMetrics) GetAnnotationSpecId
func (x *TextSentimentEvaluationMetrics) GetAnnotationSpecId() []stringDeprecated: Marked as deprecated in google/cloud/automl/v1beta1/text_sentiment.proto.
func (*TextSentimentEvaluationMetrics) GetConfusionMatrix
func (x *TextSentimentEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrixfunc (*TextSentimentEvaluationMetrics) GetF1Score
func (x *TextSentimentEvaluationMetrics) GetF1Score() float32func (*TextSentimentEvaluationMetrics) GetLinearKappa
func (x *TextSentimentEvaluationMetrics) GetLinearKappa() float32func (*TextSentimentEvaluationMetrics) GetMeanAbsoluteError
func (x *TextSentimentEvaluationMetrics) GetMeanAbsoluteError() float32func (*TextSentimentEvaluationMetrics) GetMeanSquaredError
func (x *TextSentimentEvaluationMetrics) GetMeanSquaredError() float32func (*TextSentimentEvaluationMetrics) GetPrecision
func (x *TextSentimentEvaluationMetrics) GetPrecision() float32func (*TextSentimentEvaluationMetrics) GetQuadraticKappa
func (x *TextSentimentEvaluationMetrics) GetQuadraticKappa() float32func (*TextSentimentEvaluationMetrics) GetRecall
func (x *TextSentimentEvaluationMetrics) GetRecall() float32func (*TextSentimentEvaluationMetrics) ProtoMessage
func (*TextSentimentEvaluationMetrics) ProtoMessage()func (*TextSentimentEvaluationMetrics) ProtoReflect
func (x *TextSentimentEvaluationMetrics) ProtoReflect() protoreflect.Messagefunc (*TextSentimentEvaluationMetrics) Reset
func (x *TextSentimentEvaluationMetrics) Reset()func (*TextSentimentEvaluationMetrics) String
func (x *TextSentimentEvaluationMetrics) String() stringTextSentimentModelMetadata
type TextSentimentModelMetadata struct {
// contains filtered or unexported fields
}Model metadata that is specific to text sentiment.
func (*TextSentimentModelMetadata) Descriptor
func (*TextSentimentModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use TextSentimentModelMetadata.ProtoReflect.Descriptor instead.
func (*TextSentimentModelMetadata) ProtoMessage
func (*TextSentimentModelMetadata) ProtoMessage()func (*TextSentimentModelMetadata) ProtoReflect
func (x *TextSentimentModelMetadata) ProtoReflect() protoreflect.Messagefunc (*TextSentimentModelMetadata) Reset
func (x *TextSentimentModelMetadata) Reset()func (*TextSentimentModelMetadata) String
func (x *TextSentimentModelMetadata) String() stringTextSnippet
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"`
// contains filtered or unexported fields
}A representation of a text snippet.
func (*TextSnippet) Descriptor
func (*TextSnippet) Descriptor() ([]byte, []int)Deprecated: Use TextSnippet.ProtoReflect.Descriptor instead.
func (*TextSnippet) GetContent
func (x *TextSnippet) GetContent() stringfunc (*TextSnippet) GetContentUri
func (x *TextSnippet) GetContentUri() stringfunc (*TextSnippet) GetMimeType
func (x *TextSnippet) GetMimeType() stringfunc (*TextSnippet) ProtoMessage
func (*TextSnippet) ProtoMessage()func (*TextSnippet) ProtoReflect
func (x *TextSnippet) ProtoReflect() protoreflect.Messagefunc (*TextSnippet) Reset
func (x *TextSnippet) Reset()func (*TextSnippet) String
func (x *TextSnippet) String() stringTimeSegment
type TimeSegment struct {
// Start of the time segment (inclusive), represented as the duration since
// the example start.
StartTimeOffset *durationpb.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 *durationpb.Duration `protobuf:"bytes,2,opt,name=end_time_offset,json=endTimeOffset,proto3" json:"end_time_offset,omitempty"`
// contains filtered or unexported fields
}A time period inside of an example that has a time dimension (e.g. video).
func (*TimeSegment) Descriptor
func (*TimeSegment) Descriptor() ([]byte, []int)Deprecated: Use TimeSegment.ProtoReflect.Descriptor instead.
func (*TimeSegment) GetEndTimeOffset
func (x *TimeSegment) GetEndTimeOffset() *durationpb.Durationfunc (*TimeSegment) GetStartTimeOffset
func (x *TimeSegment) GetStartTimeOffset() *durationpb.Durationfunc (*TimeSegment) ProtoMessage
func (*TimeSegment) ProtoMessage()func (*TimeSegment) ProtoReflect
func (x *TimeSegment) ProtoReflect() protoreflect.Messagefunc (*TimeSegment) Reset
func (x *TimeSegment) Reset()func (*TimeSegment) String
func (x *TimeSegment) String() stringTimestampStats
type TimestampStats struct {
GranularStats map[string]*TimestampStats_GranularStats "" /* 188 byte string literal not displayed */
}The data statistics of a series of TIMESTAMP values.
func (*TimestampStats) Descriptor
func (*TimestampStats) Descriptor() ([]byte, []int)Deprecated: Use TimestampStats.ProtoReflect.Descriptor instead.
func (*TimestampStats) GetGranularStats
func (x *TimestampStats) GetGranularStats() map[string]*TimestampStats_GranularStatsfunc (*TimestampStats) ProtoMessage
func (*TimestampStats) ProtoMessage()func (*TimestampStats) ProtoReflect
func (x *TimestampStats) ProtoReflect() protoreflect.Messagefunc (*TimestampStats) Reset
func (x *TimestampStats) Reset()func (*TimestampStats) String
func (x *TimestampStats) String() stringTimestampStats_GranularStats
type TimestampStats_GranularStats struct {
Buckets map[int32]int64 "" /* 157 byte string literal not displayed */
}Stats split by a defined in context granularity.
func (*TimestampStats_GranularStats) Descriptor
func (*TimestampStats_GranularStats) Descriptor() ([]byte, []int)Deprecated: Use TimestampStats_GranularStats.ProtoReflect.Descriptor instead.
func (*TimestampStats_GranularStats) GetBuckets
func (x *TimestampStats_GranularStats) GetBuckets() map[int32]int64func (*TimestampStats_GranularStats) ProtoMessage
func (*TimestampStats_GranularStats) ProtoMessage()func (*TimestampStats_GranularStats) ProtoReflect
func (x *TimestampStats_GranularStats) ProtoReflect() protoreflect.Messagefunc (*TimestampStats_GranularStats) Reset
func (x *TimestampStats_GranularStats) Reset()func (*TimestampStats_GranularStats) String
func (x *TimestampStats_GranularStats) String() stringTranslationAnnotation
type TranslationAnnotation struct {
// Output only . The translated content.
TranslatedContent *TextSnippet `protobuf:"bytes,1,opt,name=translated_content,json=translatedContent,proto3" json:"translated_content,omitempty"`
// contains filtered or unexported fields
}Annotation details specific to translation.
func (*TranslationAnnotation) Descriptor
func (*TranslationAnnotation) Descriptor() ([]byte, []int)Deprecated: Use TranslationAnnotation.ProtoReflect.Descriptor instead.
func (*TranslationAnnotation) GetTranslatedContent
func (x *TranslationAnnotation) GetTranslatedContent() *TextSnippetfunc (*TranslationAnnotation) ProtoMessage
func (*TranslationAnnotation) ProtoMessage()func (*TranslationAnnotation) ProtoReflect
func (x *TranslationAnnotation) ProtoReflect() protoreflect.Messagefunc (*TranslationAnnotation) Reset
func (x *TranslationAnnotation) Reset()func (*TranslationAnnotation) String
func (x *TranslationAnnotation) String() stringTranslationDatasetMetadata
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"`
// contains filtered or unexported fields
}Dataset metadata that is specific to translation.
func (*TranslationDatasetMetadata) Descriptor
func (*TranslationDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use TranslationDatasetMetadata.ProtoReflect.Descriptor instead.
func (*TranslationDatasetMetadata) GetSourceLanguageCode
func (x *TranslationDatasetMetadata) GetSourceLanguageCode() stringfunc (*TranslationDatasetMetadata) GetTargetLanguageCode
func (x *TranslationDatasetMetadata) GetTargetLanguageCode() stringfunc (*TranslationDatasetMetadata) ProtoMessage
func (*TranslationDatasetMetadata) ProtoMessage()func (*TranslationDatasetMetadata) ProtoReflect
func (x *TranslationDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*TranslationDatasetMetadata) Reset
func (x *TranslationDatasetMetadata) Reset()func (*TranslationDatasetMetadata) String
func (x *TranslationDatasetMetadata) String() stringTranslationEvaluationMetrics
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"`
// contains filtered or unexported fields
}Evaluation metrics for the dataset.
func (*TranslationEvaluationMetrics) Descriptor
func (*TranslationEvaluationMetrics) Descriptor() ([]byte, []int)Deprecated: Use TranslationEvaluationMetrics.ProtoReflect.Descriptor instead.
func (*TranslationEvaluationMetrics) GetBaseBleuScore
func (x *TranslationEvaluationMetrics) GetBaseBleuScore() float64func (*TranslationEvaluationMetrics) GetBleuScore
func (x *TranslationEvaluationMetrics) GetBleuScore() float64func (*TranslationEvaluationMetrics) ProtoMessage
func (*TranslationEvaluationMetrics) ProtoMessage()func (*TranslationEvaluationMetrics) ProtoReflect
func (x *TranslationEvaluationMetrics) ProtoReflect() protoreflect.Messagefunc (*TranslationEvaluationMetrics) Reset
func (x *TranslationEvaluationMetrics) Reset()func (*TranslationEvaluationMetrics) String
func (x *TranslationEvaluationMetrics) String() stringTranslationModelMetadata
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"`
// contains filtered or unexported fields
}Model metadata that is specific to translation.
func (*TranslationModelMetadata) Descriptor
func (*TranslationModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use TranslationModelMetadata.ProtoReflect.Descriptor instead.
func (*TranslationModelMetadata) GetBaseModel
func (x *TranslationModelMetadata) GetBaseModel() stringfunc (*TranslationModelMetadata) GetSourceLanguageCode
func (x *TranslationModelMetadata) GetSourceLanguageCode() stringfunc (*TranslationModelMetadata) GetTargetLanguageCode
func (x *TranslationModelMetadata) GetTargetLanguageCode() stringfunc (*TranslationModelMetadata) ProtoMessage
func (*TranslationModelMetadata) ProtoMessage()func (*TranslationModelMetadata) ProtoReflect
func (x *TranslationModelMetadata) ProtoReflect() protoreflect.Messagefunc (*TranslationModelMetadata) Reset
func (x *TranslationModelMetadata) Reset()func (*TranslationModelMetadata) String
func (x *TranslationModelMetadata) String() stringTypeCode
type TypeCode int32TypeCode is used as a part of
[DataType][google.cloud.automl.v1beta1.DataType].
TypeCode_TYPE_CODE_UNSPECIFIED, TypeCode_FLOAT64, TypeCode_TIMESTAMP, TypeCode_STRING, TypeCode_ARRAY, TypeCode_STRUCT, TypeCode_CATEGORY
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) Descriptor
func (TypeCode) Descriptor() protoreflect.EnumDescriptorfunc (TypeCode) Enum
func (TypeCode) EnumDescriptor
Deprecated: Use TypeCode.Descriptor instead.
func (TypeCode) Number
func (x TypeCode) Number() protoreflect.EnumNumberfunc (TypeCode) String
func (TypeCode) Type
func (TypeCode) Type() protoreflect.EnumTypeUndeployModelOperationMetadata
type UndeployModelOperationMetadata struct {
// contains filtered or unexported fields
}Details of UndeployModel operation.
func (*UndeployModelOperationMetadata) Descriptor
func (*UndeployModelOperationMetadata) Descriptor() ([]byte, []int)Deprecated: Use UndeployModelOperationMetadata.ProtoReflect.Descriptor instead.
func (*UndeployModelOperationMetadata) ProtoMessage
func (*UndeployModelOperationMetadata) ProtoMessage()func (*UndeployModelOperationMetadata) ProtoReflect
func (x *UndeployModelOperationMetadata) ProtoReflect() protoreflect.Messagefunc (*UndeployModelOperationMetadata) Reset
func (x *UndeployModelOperationMetadata) Reset()func (*UndeployModelOperationMetadata) String
func (x *UndeployModelOperationMetadata) String() stringUndeployModelRequest
type UndeployModelRequest struct {
// Required. Resource name of the model to undeploy.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.UndeployModel][google.cloud.automl.v1beta1.AutoMl.UndeployModel].
func (*UndeployModelRequest) Descriptor
func (*UndeployModelRequest) Descriptor() ([]byte, []int)Deprecated: Use UndeployModelRequest.ProtoReflect.Descriptor instead.
func (*UndeployModelRequest) GetName
func (x *UndeployModelRequest) GetName() stringfunc (*UndeployModelRequest) ProtoMessage
func (*UndeployModelRequest) ProtoMessage()func (*UndeployModelRequest) ProtoReflect
func (x *UndeployModelRequest) ProtoReflect() protoreflect.Messagefunc (*UndeployModelRequest) Reset
func (x *UndeployModelRequest) Reset()func (*UndeployModelRequest) String
func (x *UndeployModelRequest) String() stringUnimplementedAutoMlServer
type UnimplementedAutoMlServer struct {
}UnimplementedAutoMlServer can be embedded to have forward compatible implementations.
func (*UnimplementedAutoMlServer) CreateDataset
func (*UnimplementedAutoMlServer) CreateDataset(context.Context, *CreateDatasetRequest) (*Dataset, error)func (*UnimplementedAutoMlServer) CreateModel
func (*UnimplementedAutoMlServer) CreateModel(context.Context, *CreateModelRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) DeleteDataset
func (*UnimplementedAutoMlServer) DeleteDataset(context.Context, *DeleteDatasetRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) DeleteModel
func (*UnimplementedAutoMlServer) DeleteModel(context.Context, *DeleteModelRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) DeployModel
func (*UnimplementedAutoMlServer) DeployModel(context.Context, *DeployModelRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) ExportData
func (*UnimplementedAutoMlServer) ExportData(context.Context, *ExportDataRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) ExportEvaluatedExamples
func (*UnimplementedAutoMlServer) ExportEvaluatedExamples(context.Context, *ExportEvaluatedExamplesRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) ExportModel
func (*UnimplementedAutoMlServer) ExportModel(context.Context, *ExportModelRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) GetAnnotationSpec
func (*UnimplementedAutoMlServer) GetAnnotationSpec(context.Context, *GetAnnotationSpecRequest) (*AnnotationSpec, error)func (*UnimplementedAutoMlServer) GetColumnSpec
func (*UnimplementedAutoMlServer) GetColumnSpec(context.Context, *GetColumnSpecRequest) (*ColumnSpec, error)func (*UnimplementedAutoMlServer) GetDataset
func (*UnimplementedAutoMlServer) GetDataset(context.Context, *GetDatasetRequest) (*Dataset, error)func (*UnimplementedAutoMlServer) GetModel
func (*UnimplementedAutoMlServer) GetModel(context.Context, *GetModelRequest) (*Model, error)func (*UnimplementedAutoMlServer) GetModelEvaluation
func (*UnimplementedAutoMlServer) GetModelEvaluation(context.Context, *GetModelEvaluationRequest) (*ModelEvaluation, error)func (*UnimplementedAutoMlServer) GetTableSpec
func (*UnimplementedAutoMlServer) GetTableSpec(context.Context, *GetTableSpecRequest) (*TableSpec, error)func (*UnimplementedAutoMlServer) ImportData
func (*UnimplementedAutoMlServer) ImportData(context.Context, *ImportDataRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) ListColumnSpecs
func (*UnimplementedAutoMlServer) ListColumnSpecs(context.Context, *ListColumnSpecsRequest) (*ListColumnSpecsResponse, error)func (*UnimplementedAutoMlServer) ListDatasets
func (*UnimplementedAutoMlServer) ListDatasets(context.Context, *ListDatasetsRequest) (*ListDatasetsResponse, error)func (*UnimplementedAutoMlServer) ListModelEvaluations
func (*UnimplementedAutoMlServer) ListModelEvaluations(context.Context, *ListModelEvaluationsRequest) (*ListModelEvaluationsResponse, error)func (*UnimplementedAutoMlServer) ListModels
func (*UnimplementedAutoMlServer) ListModels(context.Context, *ListModelsRequest) (*ListModelsResponse, error)func (*UnimplementedAutoMlServer) ListTableSpecs
func (*UnimplementedAutoMlServer) ListTableSpecs(context.Context, *ListTableSpecsRequest) (*ListTableSpecsResponse, error)func (*UnimplementedAutoMlServer) UndeployModel
func (*UnimplementedAutoMlServer) UndeployModel(context.Context, *UndeployModelRequest) (*longrunningpb.Operation, error)func (*UnimplementedAutoMlServer) UpdateColumnSpec
func (*UnimplementedAutoMlServer) UpdateColumnSpec(context.Context, *UpdateColumnSpecRequest) (*ColumnSpec, error)func (*UnimplementedAutoMlServer) UpdateDataset
func (*UnimplementedAutoMlServer) UpdateDataset(context.Context, *UpdateDatasetRequest) (*Dataset, error)func (*UnimplementedAutoMlServer) UpdateTableSpec
func (*UnimplementedAutoMlServer) UpdateTableSpec(context.Context, *UpdateTableSpecRequest) (*TableSpec, error)UnimplementedPredictionServiceServer
type UnimplementedPredictionServiceServer struct {
}UnimplementedPredictionServiceServer can be embedded to have forward compatible implementations.
func (*UnimplementedPredictionServiceServer) BatchPredict
func (*UnimplementedPredictionServiceServer) BatchPredict(context.Context, *BatchPredictRequest) (*longrunningpb.Operation, error)func (*UnimplementedPredictionServiceServer) Predict
func (*UnimplementedPredictionServiceServer) Predict(context.Context, *PredictRequest) (*PredictResponse, error)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 *fieldmaskpb.FieldMask `protobuf:"bytes,2,opt,name=update_mask,json=updateMask,proto3" json:"update_mask,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.UpdateColumnSpec][google.cloud.automl.v1beta1.AutoMl.UpdateColumnSpec]
func (*UpdateColumnSpecRequest) Descriptor
func (*UpdateColumnSpecRequest) Descriptor() ([]byte, []int)Deprecated: Use UpdateColumnSpecRequest.ProtoReflect.Descriptor instead.
func (*UpdateColumnSpecRequest) GetColumnSpec
func (x *UpdateColumnSpecRequest) GetColumnSpec() *ColumnSpecfunc (*UpdateColumnSpecRequest) GetUpdateMask
func (x *UpdateColumnSpecRequest) GetUpdateMask() *fieldmaskpb.FieldMaskfunc (*UpdateColumnSpecRequest) ProtoMessage
func (*UpdateColumnSpecRequest) ProtoMessage()func (*UpdateColumnSpecRequest) ProtoReflect
func (x *UpdateColumnSpecRequest) ProtoReflect() protoreflect.Messagefunc (*UpdateColumnSpecRequest) Reset
func (x *UpdateColumnSpecRequest) Reset()func (*UpdateColumnSpecRequest) String
func (x *UpdateColumnSpecRequest) String() stringUpdateDatasetRequest
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 *fieldmaskpb.FieldMask `protobuf:"bytes,2,opt,name=update_mask,json=updateMask,proto3" json:"update_mask,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1beta1.AutoMl.UpdateDataset]
func (*UpdateDatasetRequest) Descriptor
func (*UpdateDatasetRequest) Descriptor() ([]byte, []int)Deprecated: Use UpdateDatasetRequest.ProtoReflect.Descriptor instead.
func (*UpdateDatasetRequest) GetDataset
func (x *UpdateDatasetRequest) GetDataset() *Datasetfunc (*UpdateDatasetRequest) GetUpdateMask
func (x *UpdateDatasetRequest) GetUpdateMask() *fieldmaskpb.FieldMaskfunc (*UpdateDatasetRequest) ProtoMessage
func (*UpdateDatasetRequest) ProtoMessage()func (*UpdateDatasetRequest) ProtoReflect
func (x *UpdateDatasetRequest) ProtoReflect() protoreflect.Messagefunc (*UpdateDatasetRequest) Reset
func (x *UpdateDatasetRequest) Reset()func (*UpdateDatasetRequest) String
func (x *UpdateDatasetRequest) String() stringUpdateTableSpecRequest
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 *fieldmaskpb.FieldMask `protobuf:"bytes,2,opt,name=update_mask,json=updateMask,proto3" json:"update_mask,omitempty"`
// contains filtered or unexported fields
}Request message for [AutoMl.UpdateTableSpec][google.cloud.automl.v1beta1.AutoMl.UpdateTableSpec]
func (*UpdateTableSpecRequest) Descriptor
func (*UpdateTableSpecRequest) Descriptor() ([]byte, []int)Deprecated: Use UpdateTableSpecRequest.ProtoReflect.Descriptor instead.
func (*UpdateTableSpecRequest) GetTableSpec
func (x *UpdateTableSpecRequest) GetTableSpec() *TableSpecfunc (*UpdateTableSpecRequest) GetUpdateMask
func (x *UpdateTableSpecRequest) GetUpdateMask() *fieldmaskpb.FieldMaskfunc (*UpdateTableSpecRequest) ProtoMessage
func (*UpdateTableSpecRequest) ProtoMessage()func (*UpdateTableSpecRequest) ProtoReflect
func (x *UpdateTableSpecRequest) ProtoReflect() protoreflect.Messagefunc (*UpdateTableSpecRequest) Reset
func (x *UpdateTableSpecRequest) Reset()func (*UpdateTableSpecRequest) String
func (x *UpdateTableSpecRequest) String() stringVideoClassificationAnnotation
type VideoClassificationAnnotation struct {
Type string `protobuf:"bytes,1,opt,name=type,proto3" json:"type,omitempty"`
ClassificationAnnotation *ClassificationAnnotation "" /* 133 byte string literal not displayed */
TimeSegment *TimeSegment `protobuf:"bytes,3,opt,name=time_segment,json=timeSegment,proto3" json:"time_segment,omitempty"`
}Contains annotation details specific to video classification.
func (*VideoClassificationAnnotation) Descriptor
func (*VideoClassificationAnnotation) Descriptor() ([]byte, []int)Deprecated: Use VideoClassificationAnnotation.ProtoReflect.Descriptor instead.
func (*VideoClassificationAnnotation) GetClassificationAnnotation
func (x *VideoClassificationAnnotation) GetClassificationAnnotation() *ClassificationAnnotationfunc (*VideoClassificationAnnotation) GetTimeSegment
func (x *VideoClassificationAnnotation) GetTimeSegment() *TimeSegmentfunc (*VideoClassificationAnnotation) GetType
func (x *VideoClassificationAnnotation) GetType() stringfunc (*VideoClassificationAnnotation) ProtoMessage
func (*VideoClassificationAnnotation) ProtoMessage()func (*VideoClassificationAnnotation) ProtoReflect
func (x *VideoClassificationAnnotation) ProtoReflect() protoreflect.Messagefunc (*VideoClassificationAnnotation) Reset
func (x *VideoClassificationAnnotation) Reset()func (*VideoClassificationAnnotation) String
func (x *VideoClassificationAnnotation) String() stringVideoClassificationDatasetMetadata
type VideoClassificationDatasetMetadata struct {
// contains filtered or unexported fields
}Dataset metadata specific to video classification. All Video Classification datasets are treated as multi label.
func (*VideoClassificationDatasetMetadata) Descriptor
func (*VideoClassificationDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use VideoClassificationDatasetMetadata.ProtoReflect.Descriptor instead.
func (*VideoClassificationDatasetMetadata) ProtoMessage
func (*VideoClassificationDatasetMetadata) ProtoMessage()func (*VideoClassificationDatasetMetadata) ProtoReflect
func (x *VideoClassificationDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*VideoClassificationDatasetMetadata) Reset
func (x *VideoClassificationDatasetMetadata) Reset()func (*VideoClassificationDatasetMetadata) String
func (x *VideoClassificationDatasetMetadata) String() stringVideoClassificationModelMetadata
type VideoClassificationModelMetadata struct {
// contains filtered or unexported fields
}Model metadata specific to video classification.
func (*VideoClassificationModelMetadata) Descriptor
func (*VideoClassificationModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use VideoClassificationModelMetadata.ProtoReflect.Descriptor instead.
func (*VideoClassificationModelMetadata) ProtoMessage
func (*VideoClassificationModelMetadata) ProtoMessage()func (*VideoClassificationModelMetadata) ProtoReflect
func (x *VideoClassificationModelMetadata) ProtoReflect() protoreflect.Messagefunc (*VideoClassificationModelMetadata) Reset
func (x *VideoClassificationModelMetadata) Reset()func (*VideoClassificationModelMetadata) String
func (x *VideoClassificationModelMetadata) String() stringVideoObjectTrackingAnnotation
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 *durationpb.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"`
// contains filtered or unexported fields
}Annotation details for video object tracking.
func (*VideoObjectTrackingAnnotation) Descriptor
func (*VideoObjectTrackingAnnotation) Descriptor() ([]byte, []int)Deprecated: Use VideoObjectTrackingAnnotation.ProtoReflect.Descriptor instead.
func (*VideoObjectTrackingAnnotation) GetBoundingBox
func (x *VideoObjectTrackingAnnotation) GetBoundingBox() *BoundingPolyfunc (*VideoObjectTrackingAnnotation) GetInstanceId
func (x *VideoObjectTrackingAnnotation) GetInstanceId() stringfunc (*VideoObjectTrackingAnnotation) GetScore
func (x *VideoObjectTrackingAnnotation) GetScore() float32func (*VideoObjectTrackingAnnotation) GetTimeOffset
func (x *VideoObjectTrackingAnnotation) GetTimeOffset() *durationpb.Durationfunc (*VideoObjectTrackingAnnotation) ProtoMessage
func (*VideoObjectTrackingAnnotation) ProtoMessage()func (*VideoObjectTrackingAnnotation) ProtoReflect
func (x *VideoObjectTrackingAnnotation) ProtoReflect() protoreflect.Messagefunc (*VideoObjectTrackingAnnotation) Reset
func (x *VideoObjectTrackingAnnotation) Reset()func (*VideoObjectTrackingAnnotation) String
func (x *VideoObjectTrackingAnnotation) String() stringVideoObjectTrackingDatasetMetadata
type VideoObjectTrackingDatasetMetadata struct {
// contains filtered or unexported fields
}Dataset metadata specific to video object tracking.
func (*VideoObjectTrackingDatasetMetadata) Descriptor
func (*VideoObjectTrackingDatasetMetadata) Descriptor() ([]byte, []int)Deprecated: Use VideoObjectTrackingDatasetMetadata.ProtoReflect.Descriptor instead.
func (*VideoObjectTrackingDatasetMetadata) ProtoMessage
func (*VideoObjectTrackingDatasetMetadata) ProtoMessage()func (*VideoObjectTrackingDatasetMetadata) ProtoReflect
func (x *VideoObjectTrackingDatasetMetadata) ProtoReflect() protoreflect.Messagefunc (*VideoObjectTrackingDatasetMetadata) Reset
func (x *VideoObjectTrackingDatasetMetadata) Reset()func (*VideoObjectTrackingDatasetMetadata) String
func (x *VideoObjectTrackingDatasetMetadata) String() stringVideoObjectTrackingEvaluationMetrics
type VideoObjectTrackingEvaluationMetrics struct {
EvaluatedFrameCount int32 `protobuf:"varint,1,opt,name=evaluated_frame_count,json=evaluatedFrameCount,proto3" json:"evaluated_frame_count,omitempty"`
EvaluatedBoundingBoxCount int32 "" /* 141 byte string literal not displayed */
BoundingBoxMetricsEntries []*BoundingBoxMetricsEntry "" /* 140 byte string literal not displayed */
BoundingBoxMeanAveragePrecision float32 "" /* 162 byte string literal not displayed */
}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)Deprecated: Use VideoObjectTrackingEvaluationMetrics.ProtoReflect.Descriptor instead.
func (*VideoObjectTrackingEvaluationMetrics) GetBoundingBoxMeanAveragePrecision
func (x *VideoObjectTrackingEvaluationMetrics) GetBoundingBoxMeanAveragePrecision() float32func (*VideoObjectTrackingEvaluationMetrics) GetBoundingBoxMetricsEntries
func (x *VideoObjectTrackingEvaluationMetrics) GetBoundingBoxMetricsEntries() []*BoundingBoxMetricsEntryfunc (*VideoObjectTrackingEvaluationMetrics) GetEvaluatedBoundingBoxCount
func (x *VideoObjectTrackingEvaluationMetrics) GetEvaluatedBoundingBoxCount() int32func (*VideoObjectTrackingEvaluationMetrics) GetEvaluatedFrameCount
func (x *VideoObjectTrackingEvaluationMetrics) GetEvaluatedFrameCount() int32func (*VideoObjectTrackingEvaluationMetrics) ProtoMessage
func (*VideoObjectTrackingEvaluationMetrics) ProtoMessage()func (*VideoObjectTrackingEvaluationMetrics) ProtoReflect
func (x *VideoObjectTrackingEvaluationMetrics) ProtoReflect() protoreflect.Messagefunc (*VideoObjectTrackingEvaluationMetrics) Reset
func (x *VideoObjectTrackingEvaluationMetrics) Reset()func (*VideoObjectTrackingEvaluationMetrics) String
func (x *VideoObjectTrackingEvaluationMetrics) String() stringVideoObjectTrackingModelMetadata
type VideoObjectTrackingModelMetadata struct {
// contains filtered or unexported fields
}Model metadata specific to video object tracking.
func (*VideoObjectTrackingModelMetadata) Descriptor
func (*VideoObjectTrackingModelMetadata) Descriptor() ([]byte, []int)Deprecated: Use VideoObjectTrackingModelMetadata.ProtoReflect.Descriptor instead.
func (*VideoObjectTrackingModelMetadata) ProtoMessage
func (*VideoObjectTrackingModelMetadata) ProtoMessage()func (*VideoObjectTrackingModelMetadata) ProtoReflect
func (x *VideoObjectTrackingModelMetadata) ProtoReflect() protoreflect.Messagefunc (*VideoObjectTrackingModelMetadata) Reset
func (x *VideoObjectTrackingModelMetadata) Reset()func (*VideoObjectTrackingModelMetadata) String
func (x *VideoObjectTrackingModelMetadata) String() string