genproto: google.golang.org/genproto/googleapis/cloud/automl/v1beta1 Index | Files

package automl

import "google.golang.org/genproto/googleapis/cloud/automl/v1beta1"

Index

Package Files

annotation_payload.pb.go annotation_spec.pb.go classification.pb.go column_spec.pb.go data_items.pb.go data_stats.pb.go data_types.pb.go dataset.pb.go detection.pb.go geometry.pb.go image.pb.go io.pb.go model.pb.go model_evaluation.pb.go operations.pb.go prediction_service.pb.go ranges.pb.go regression.pb.go service.pb.go table_spec.pb.go tables.pb.go temporal.pb.go text.pb.go text_extraction.pb.go text_segment.pb.go text_sentiment.pb.go translation.pb.go video.pb.go

Variables

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.

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.

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.

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.

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.

var File_google_cloud_automl_v1beta1_annotation_payload_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_annotation_spec_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_classification_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_column_spec_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_data_items_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_data_stats_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_data_types_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_dataset_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_detection_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_geometry_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_image_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_io_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_model_evaluation_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_model_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_operations_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_prediction_service_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_ranges_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_regression_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_service_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_table_spec_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_tables_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_temporal_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_text_extraction_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_text_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_text_segment_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_text_sentiment_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_translation_proto protoreflect.FileDescriptor
var File_google_cloud_automl_v1beta1_video_proto protoreflect.FileDescriptor

func RegisterAutoMlServer Uses

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

func RegisterPredictionServiceServer Uses

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

type AnnotationPayload Uses

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 Uses

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

Deprecated: Use AnnotationPayload.ProtoReflect.Descriptor instead.

func (*AnnotationPayload) GetAnnotationSpecId Uses

func (x *AnnotationPayload) GetAnnotationSpecId() string

func (*AnnotationPayload) GetClassification Uses

func (x *AnnotationPayload) GetClassification() *ClassificationAnnotation

func (*AnnotationPayload) GetDetail Uses

func (m *AnnotationPayload) GetDetail() isAnnotationPayload_Detail

func (*AnnotationPayload) GetDisplayName Uses

func (x *AnnotationPayload) GetDisplayName() string

func (*AnnotationPayload) GetImageObjectDetection Uses

func (x *AnnotationPayload) GetImageObjectDetection() *ImageObjectDetectionAnnotation

func (*AnnotationPayload) GetTables Uses

func (x *AnnotationPayload) GetTables() *TablesAnnotation

func (*AnnotationPayload) GetTextExtraction Uses

func (x *AnnotationPayload) GetTextExtraction() *TextExtractionAnnotation

func (*AnnotationPayload) GetTextSentiment Uses

func (x *AnnotationPayload) GetTextSentiment() *TextSentimentAnnotation

func (*AnnotationPayload) GetTranslation Uses

func (x *AnnotationPayload) GetTranslation() *TranslationAnnotation

func (*AnnotationPayload) GetVideoClassification Uses

func (x *AnnotationPayload) GetVideoClassification() *VideoClassificationAnnotation

func (*AnnotationPayload) GetVideoObjectTracking Uses

func (x *AnnotationPayload) GetVideoObjectTracking() *VideoObjectTrackingAnnotation

func (*AnnotationPayload) ProtoMessage Uses

func (*AnnotationPayload) ProtoMessage()

func (*AnnotationPayload) ProtoReflect Uses

func (x *AnnotationPayload) ProtoReflect() protoreflect.Message

func (*AnnotationPayload) Reset Uses

func (x *AnnotationPayload) Reset()

func (*AnnotationPayload) String Uses

func (x *AnnotationPayload) String() string

type AnnotationPayload_Classification Uses

type AnnotationPayload_Classification struct {
    // Annotation details for content or image classification.
    Classification *ClassificationAnnotation `protobuf:"bytes,3,opt,name=classification,proto3,oneof"`
}

type AnnotationPayload_ImageObjectDetection Uses

type AnnotationPayload_ImageObjectDetection struct {
    // Annotation details for image object detection.
    ImageObjectDetection *ImageObjectDetectionAnnotation `protobuf:"bytes,4,opt,name=image_object_detection,json=imageObjectDetection,proto3,oneof"`
}

type AnnotationPayload_Tables Uses

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

type AnnotationPayload_TextExtraction Uses

type AnnotationPayload_TextExtraction struct {
    // Annotation details for text extraction.
    TextExtraction *TextExtractionAnnotation `protobuf:"bytes,6,opt,name=text_extraction,json=textExtraction,proto3,oneof"`
}

type AnnotationPayload_TextSentiment Uses

type AnnotationPayload_TextSentiment struct {
    // Annotation details for text sentiment.
    TextSentiment *TextSentimentAnnotation `protobuf:"bytes,7,opt,name=text_sentiment,json=textSentiment,proto3,oneof"`
}

type AnnotationPayload_Translation Uses

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

type AnnotationPayload_VideoClassification Uses

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"`
}

type AnnotationPayload_VideoObjectTracking Uses

type AnnotationPayload_VideoObjectTracking struct {
    // Annotation details for video object tracking.
    VideoObjectTracking *VideoObjectTrackingAnnotation `protobuf:"bytes,8,opt,name=video_object_tracking,json=videoObjectTracking,proto3,oneof"`
}

type AnnotationSpec Uses

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 Uses

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

Deprecated: Use AnnotationSpec.ProtoReflect.Descriptor instead.

func (*AnnotationSpec) GetDisplayName Uses

func (x *AnnotationSpec) GetDisplayName() string

func (*AnnotationSpec) GetExampleCount Uses

func (x *AnnotationSpec) GetExampleCount() int32

func (*AnnotationSpec) GetName Uses

func (x *AnnotationSpec) GetName() string

func (*AnnotationSpec) ProtoMessage Uses

func (*AnnotationSpec) ProtoMessage()

func (*AnnotationSpec) ProtoReflect Uses

func (x *AnnotationSpec) ProtoReflect() protoreflect.Message

func (*AnnotationSpec) Reset Uses

func (x *AnnotationSpec) Reset()

func (*AnnotationSpec) String Uses

func (x *AnnotationSpec) String() string

type ArrayStats Uses

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 Uses

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

Deprecated: Use ArrayStats.ProtoReflect.Descriptor instead.

func (*ArrayStats) GetMemberStats Uses

func (x *ArrayStats) GetMemberStats() *DataStats

func (*ArrayStats) ProtoMessage Uses

func (*ArrayStats) ProtoMessage()

func (*ArrayStats) ProtoReflect Uses

func (x *ArrayStats) ProtoReflect() protoreflect.Message

func (*ArrayStats) Reset Uses

func (x *ArrayStats) Reset()

func (*ArrayStats) String Uses

func (x *ArrayStats) String() string

type AutoMlClient Uses

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

AutoMlClient is the client API for AutoMl service.

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

func NewAutoMlClient Uses

func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient

type AutoMlServer Uses

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

AutoMlServer is the server API for AutoMl service.

type BatchPredictInputConfig Uses

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 Uses

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

Deprecated: Use BatchPredictInputConfig.ProtoReflect.Descriptor instead.

func (*BatchPredictInputConfig) GetBigquerySource Uses

func (x *BatchPredictInputConfig) GetBigquerySource() *BigQuerySource

func (*BatchPredictInputConfig) GetGcsSource Uses

func (x *BatchPredictInputConfig) GetGcsSource() *GcsSource

func (*BatchPredictInputConfig) GetSource Uses

func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source

func (*BatchPredictInputConfig) ProtoMessage Uses

func (*BatchPredictInputConfig) ProtoMessage()

func (*BatchPredictInputConfig) ProtoReflect Uses

func (x *BatchPredictInputConfig) ProtoReflect() protoreflect.Message

func (*BatchPredictInputConfig) Reset Uses

func (x *BatchPredictInputConfig) Reset()

func (*BatchPredictInputConfig) String Uses

func (x *BatchPredictInputConfig) String() string

type BatchPredictInputConfig_BigquerySource Uses

type BatchPredictInputConfig_BigquerySource struct {
    // The BigQuery location for the input content.
    BigquerySource *BigQuerySource `protobuf:"bytes,2,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}

type BatchPredictInputConfig_GcsSource Uses

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"`
}

type BatchPredictOperationMetadata Uses

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 Uses

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

Deprecated: Use BatchPredictOperationMetadata.ProtoReflect.Descriptor instead.

func (*BatchPredictOperationMetadata) GetInputConfig Uses

func (x *BatchPredictOperationMetadata) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictOperationMetadata) GetOutputInfo Uses

func (x *BatchPredictOperationMetadata) GetOutputInfo() *BatchPredictOperationMetadata_BatchPredictOutputInfo

func (*BatchPredictOperationMetadata) ProtoMessage Uses

func (*BatchPredictOperationMetadata) ProtoMessage()

func (*BatchPredictOperationMetadata) ProtoReflect Uses

func (x *BatchPredictOperationMetadata) ProtoReflect() protoreflect.Message

func (*BatchPredictOperationMetadata) Reset Uses

func (x *BatchPredictOperationMetadata) Reset()

func (*BatchPredictOperationMetadata) String Uses

func (x *BatchPredictOperationMetadata) String() string

type BatchPredictOperationMetadata_BatchPredictOutputInfo Uses

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 Uses

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

Deprecated: Use BatchPredictOperationMetadata_BatchPredictOutputInfo.ProtoReflect.Descriptor instead.

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetBigqueryOutputDataset Uses

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetBigqueryOutputDataset() string

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory Uses

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory() string

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation Uses

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation() isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage Uses

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect Uses

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect() protoreflect.Message

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset Uses

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) String Uses

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) String() string

type BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset Uses

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"`
}

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory Uses

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"`
}

type BatchPredictOutputConfig Uses

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-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for.

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

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

       containing only `code` and `message`fields.

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

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

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

       The format of video_classification.csv is:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS

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

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

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

       The format of video_object_tracking.csv is:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS

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

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

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

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

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

       containing only `code` and `message`.

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

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

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

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

      containing only `code` and `message`.

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

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

       containing only `code` and `message`.

*  For Tables:
       Output depends on whether

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

or

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

followed by the target column with name in the format of

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

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

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

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

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

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

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

and as a value has

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

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

func (*BatchPredictOutputConfig) Descriptor Uses

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

Deprecated: Use BatchPredictOutputConfig.ProtoReflect.Descriptor instead.

func (*BatchPredictOutputConfig) GetBigqueryDestination Uses

func (x *BatchPredictOutputConfig) GetBigqueryDestination() *BigQueryDestination

func (*BatchPredictOutputConfig) GetDestination Uses

func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination

func (*BatchPredictOutputConfig) GetGcsDestination Uses

func (x *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination

func (*BatchPredictOutputConfig) ProtoMessage Uses

func (*BatchPredictOutputConfig) ProtoMessage()

func (*BatchPredictOutputConfig) ProtoReflect Uses

func (x *BatchPredictOutputConfig) ProtoReflect() protoreflect.Message

func (*BatchPredictOutputConfig) Reset Uses

func (x *BatchPredictOutputConfig) Reset()

func (*BatchPredictOutputConfig) String Uses

func (x *BatchPredictOutputConfig) String() string

type BatchPredictOutputConfig_BigqueryDestination Uses

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"`
}

type BatchPredictOutputConfig_GcsDestination Uses

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"`
}

type BatchPredictRequest Uses

type BatchPredictRequest struct {

    // Required. Name of the model requested to serve the batch prediction.
    Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
    // Required. The input configuration for batch prediction.
    InputConfig *BatchPredictInputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
    // Required. The Configuration specifying where output predictions should
    // be written.
    OutputConfig *BatchPredictOutputConfig `protobuf:"bytes,4,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
    // Required. Additional domain-specific parameters for the predictions, any string must
    // be up to 25000 characters long.
    //
    // *  For Text Classification:
    //
    //    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
    //         makes predictions for a text snippet, it will only produce results
    //         that have at least this confidence score. The default is 0.5.
    //
    // *  For Image Classification:
    //
    //    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
    //         makes predictions for an image, it will only produce results that
    //         have at least this confidence score. The default is 0.5.
    //
    // *  For Image Object Detection:
    //
    //    `score_threshold` - (float) When Model detects objects on the image,
    //        it will only produce bounding boxes which have at least this
    //        confidence score. Value in 0 to 1 range, default is 0.5.
    //    `max_bounding_box_count` - (int64) No more than this number of bounding
    //        boxes will be produced per image. Default is 100, the
    //        requested value may be limited by server.
    //
    // *  For Video Classification :
    //
    //    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
    //        makes predictions for a video, it will only produce results that
    //        have at least this confidence score. The default is 0.5.
    //    `segment_classification` - (boolean) Set to true to request
    //        segment-level classification. AutoML Video Intelligence returns
    //        labels and their confidence scores for the entire segment of the
    //        video that user specified in the request configuration.
    //        The default is "true".
    //    `shot_classification` - (boolean) Set to true to request shot-level
    //        classification. AutoML Video Intelligence determines the boundaries
    //        for each camera shot in the entire segment of the video that user
    //        specified in the request configuration. AutoML Video Intelligence
    //        then returns labels and their confidence scores for each detected
    //        shot, along with the start and end time of the shot.
    //        WARNING: Model evaluation is not done for this classification type,
    //        the quality of it depends on training data, but there are no metrics
    //        provided to describe that quality. The default is "false".
    //    `1s_interval_classification` - (boolean) Set to true to request
    //        classification for a video at one-second intervals. AutoML Video
    //        Intelligence returns labels and their confidence scores for each
    //        second of the entire segment of the video that user specified in the
    //        request configuration.
    //        WARNING: Model evaluation is not done for this classification
    //        type, the quality of it depends on training data, but there are no
    //        metrics provided to describe that quality. The default is
    //        "false".
    //
    // *  For Tables:
    //
    //    feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
    //        should be populated in the returned TablesAnnotations. The
    //        default is false.
    //
    // *  For Video Object Tracking:
    //
    //    `score_threshold` - (float) When Model detects objects on video frames,
    //        it will only produce bounding boxes which have at least this
    //        confidence score. Value in 0 to 1 range, default is 0.5.
    //    `max_bounding_box_count` - (int64) No more than this number of bounding
    //        boxes will be returned per frame. Default is 100, the requested
    //        value may be limited by server.
    //    `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
    //      at least that long as a relative value of video frame size will be
    //      returned. Value in 0 to 1 range. Default is 0.
    Params map[string]string `protobuf:"bytes,5,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // contains filtered or unexported fields
}

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

func (*BatchPredictRequest) Descriptor Uses

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

Deprecated: Use BatchPredictRequest.ProtoReflect.Descriptor instead.

func (*BatchPredictRequest) GetInputConfig Uses

func (x *BatchPredictRequest) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictRequest) GetName Uses

func (x *BatchPredictRequest) GetName() string

func (*BatchPredictRequest) GetOutputConfig Uses

func (x *BatchPredictRequest) GetOutputConfig() *BatchPredictOutputConfig

func (*BatchPredictRequest) GetParams Uses

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

func (*BatchPredictRequest) ProtoMessage Uses

func (*BatchPredictRequest) ProtoMessage()

func (*BatchPredictRequest) ProtoReflect Uses

func (x *BatchPredictRequest) ProtoReflect() protoreflect.Message

func (*BatchPredictRequest) Reset Uses

func (x *BatchPredictRequest) Reset()

func (*BatchPredictRequest) String Uses

func (x *BatchPredictRequest) String() string

type BatchPredictResult Uses

type BatchPredictResult struct {

    // Additional domain-specific prediction response metadata.
    //
    // *  For Image Object Detection:
    //  `max_bounding_box_count` - (int64) At most that many bounding boxes per
    //      image could have been returned.
    //
    // *  For Video Object Tracking:
    //  `max_bounding_box_count` - (int64) At most that many bounding boxes per
    //      frame could have been returned.
    Metadata map[string]string `protobuf:"bytes,1,rep,name=metadata,proto3" json:"metadata,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // contains filtered or unexported fields
}

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 Uses

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

Deprecated: Use BatchPredictResult.ProtoReflect.Descriptor instead.

func (*BatchPredictResult) GetMetadata Uses

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

func (*BatchPredictResult) ProtoMessage Uses

func (*BatchPredictResult) ProtoMessage()

func (*BatchPredictResult) ProtoReflect Uses

func (x *BatchPredictResult) ProtoReflect() protoreflect.Message

func (*BatchPredictResult) Reset Uses

func (x *BatchPredictResult) Reset()

func (*BatchPredictResult) String Uses

func (x *BatchPredictResult) String() string

type BigQueryDestination Uses

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 Uses

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

Deprecated: Use BigQueryDestination.ProtoReflect.Descriptor instead.

func (*BigQueryDestination) GetOutputUri Uses

func (x *BigQueryDestination) GetOutputUri() string

func (*BigQueryDestination) ProtoMessage Uses

func (*BigQueryDestination) ProtoMessage()

func (*BigQueryDestination) ProtoReflect Uses

func (x *BigQueryDestination) ProtoReflect() protoreflect.Message

func (*BigQueryDestination) Reset Uses

func (x *BigQueryDestination) Reset()

func (*BigQueryDestination) String Uses

func (x *BigQueryDestination) String() string

type BigQuerySource Uses

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 Uses

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

Deprecated: Use BigQuerySource.ProtoReflect.Descriptor instead.

func (*BigQuerySource) GetInputUri Uses

func (x *BigQuerySource) GetInputUri() string

func (*BigQuerySource) ProtoMessage Uses

func (*BigQuerySource) ProtoMessage()

func (*BigQuerySource) ProtoReflect Uses

func (x *BigQuerySource) ProtoReflect() protoreflect.Message

func (*BigQuerySource) Reset Uses

func (x *BigQuerySource) Reset()

func (*BigQuerySource) String Uses

func (x *BigQuerySource) String() string

type BoundingBoxMetricsEntry Uses

type BoundingBoxMetricsEntry struct {

    // Output only. The intersection-over-union threshold value used to compute
    // this metrics entry.
    IouThreshold float32 `protobuf:"fixed32,1,opt,name=iou_threshold,json=iouThreshold,proto3" json:"iou_threshold,omitempty"`
    // Output only. The mean average precision, most often close to au_prc.
    MeanAveragePrecision float32 `protobuf:"fixed32,2,opt,name=mean_average_precision,json=meanAveragePrecision,proto3" json:"mean_average_precision,omitempty"`
    // Output only. Metrics for each label-match confidence_threshold from
    // 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is
    // derived from them.
    ConfidenceMetricsEntries []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry `protobuf:"bytes,3,rep,name=confidence_metrics_entries,json=confidenceMetricsEntries,proto3" json:"confidence_metrics_entries,omitempty"`
    // contains filtered or unexported fields
}

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

func (*BoundingBoxMetricsEntry) Descriptor Uses

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

Deprecated: Use BoundingBoxMetricsEntry.ProtoReflect.Descriptor instead.

func (*BoundingBoxMetricsEntry) GetConfidenceMetricsEntries Uses

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

func (*BoundingBoxMetricsEntry) GetIouThreshold Uses

func (x *BoundingBoxMetricsEntry) GetIouThreshold() float32

func (*BoundingBoxMetricsEntry) GetMeanAveragePrecision Uses

func (x *BoundingBoxMetricsEntry) GetMeanAveragePrecision() float32

func (*BoundingBoxMetricsEntry) ProtoMessage Uses

func (*BoundingBoxMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry) ProtoReflect Uses

func (x *BoundingBoxMetricsEntry) ProtoReflect() protoreflect.Message

func (*BoundingBoxMetricsEntry) Reset Uses

func (x *BoundingBoxMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry) String Uses

func (x *BoundingBoxMetricsEntry) String() string

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry Uses

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 Uses

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

Deprecated: Use BoundingBoxMetricsEntry_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage Uses

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String() string

type BoundingPoly Uses

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 Uses

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

Deprecated: Use BoundingPoly.ProtoReflect.Descriptor instead.

func (*BoundingPoly) GetNormalizedVertices Uses

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

func (*BoundingPoly) ProtoMessage Uses

func (*BoundingPoly) ProtoMessage()

func (*BoundingPoly) ProtoReflect Uses

func (x *BoundingPoly) ProtoReflect() protoreflect.Message

func (*BoundingPoly) Reset Uses

func (x *BoundingPoly) Reset()

func (*BoundingPoly) String Uses

func (x *BoundingPoly) String() string

type CategoryStats Uses

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 Uses

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

Deprecated: Use CategoryStats.ProtoReflect.Descriptor instead.

func (*CategoryStats) GetTopCategoryStats Uses

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

func (*CategoryStats) ProtoMessage Uses

func (*CategoryStats) ProtoMessage()

func (*CategoryStats) ProtoReflect Uses

func (x *CategoryStats) ProtoReflect() protoreflect.Message

func (*CategoryStats) Reset Uses

func (x *CategoryStats) Reset()

func (*CategoryStats) String Uses

func (x *CategoryStats) String() string

type CategoryStats_SingleCategoryStats Uses

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 Uses

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

Deprecated: Use CategoryStats_SingleCategoryStats.ProtoReflect.Descriptor instead.

func (*CategoryStats_SingleCategoryStats) GetCount Uses

func (x *CategoryStats_SingleCategoryStats) GetCount() int64

func (*CategoryStats_SingleCategoryStats) GetValue Uses

func (x *CategoryStats_SingleCategoryStats) GetValue() string

func (*CategoryStats_SingleCategoryStats) ProtoMessage Uses

func (*CategoryStats_SingleCategoryStats) ProtoMessage()

func (*CategoryStats_SingleCategoryStats) ProtoReflect Uses

func (x *CategoryStats_SingleCategoryStats) ProtoReflect() protoreflect.Message

func (*CategoryStats_SingleCategoryStats) Reset Uses

func (x *CategoryStats_SingleCategoryStats) Reset()

func (*CategoryStats_SingleCategoryStats) String Uses

func (x *CategoryStats_SingleCategoryStats) String() string

type ClassificationAnnotation Uses

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 Uses

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

Deprecated: Use ClassificationAnnotation.ProtoReflect.Descriptor instead.

func (*ClassificationAnnotation) GetScore Uses

func (x *ClassificationAnnotation) GetScore() float32

func (*ClassificationAnnotation) ProtoMessage Uses

func (*ClassificationAnnotation) ProtoMessage()

func (*ClassificationAnnotation) ProtoReflect Uses

func (x *ClassificationAnnotation) ProtoReflect() protoreflect.Message

func (*ClassificationAnnotation) Reset Uses

func (x *ClassificationAnnotation) Reset()

func (*ClassificationAnnotation) String Uses

func (x *ClassificationAnnotation) String() string

type ClassificationEvaluationMetrics Uses

type ClassificationEvaluationMetrics struct {

    // Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
    // for the overall evaluation.
    AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`
    // Output only. The Area Under Precision-Recall Curve metric based on priors.
    // Micro-averaged for the overall evaluation.
    // Deprecated.
    //
    // Deprecated: Do not use.
    BaseAuPrc float32 `protobuf:"fixed32,2,opt,name=base_au_prc,json=baseAuPrc,proto3" json:"base_au_prc,omitempty"`
    // Output only. The Area Under Receiver Operating Characteristic curve metric.
    // Micro-averaged for the overall evaluation.
    AuRoc float32 `protobuf:"fixed32,6,opt,name=au_roc,json=auRoc,proto3" json:"au_roc,omitempty"`
    // Output only. The Log Loss metric.
    LogLoss float32 `protobuf:"fixed32,7,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"`
    // Output only. Metrics for each confidence_threshold in
    // 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and
    // position_threshold = INT32_MAX_VALUE.
    // ROC and precision-recall curves, and other aggregated metrics are derived
    // from them. The confidence metrics entries may also be supplied for
    // additional values of position_threshold, but from these no aggregated
    // metrics are computed.
    ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry `protobuf:"bytes,3,rep,name=confidence_metrics_entry,json=confidenceMetricsEntry,proto3" json:"confidence_metrics_entry,omitempty"`
    // Output only. Confusion matrix of the evaluation.
    // Only set for MULTICLASS classification problems where number
    // of labels is no more than 10.
    // Only set for model level evaluation, not for evaluation per label.
    ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`
    // Output only. The annotation spec ids used for this evaluation.
    AnnotationSpecId []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
    // contains filtered or unexported fields
}

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 Uses

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

Deprecated: Use ClassificationEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics) GetAnnotationSpecId Uses

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

func (*ClassificationEvaluationMetrics) GetAuPrc Uses

func (x *ClassificationEvaluationMetrics) GetAuPrc() float32

func (*ClassificationEvaluationMetrics) GetAuRoc Uses

func (x *ClassificationEvaluationMetrics) GetAuRoc() float32

func (*ClassificationEvaluationMetrics) GetBaseAuPrc Uses

func (x *ClassificationEvaluationMetrics) GetBaseAuPrc() float32

Deprecated: Do not use.

func (*ClassificationEvaluationMetrics) GetConfidenceMetricsEntry Uses

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

func (*ClassificationEvaluationMetrics) GetConfusionMatrix Uses

func (x *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix

func (*ClassificationEvaluationMetrics) GetLogLoss Uses

func (x *ClassificationEvaluationMetrics) GetLogLoss() float32

func (*ClassificationEvaluationMetrics) ProtoMessage Uses

func (*ClassificationEvaluationMetrics) ProtoMessage()

func (*ClassificationEvaluationMetrics) ProtoReflect Uses

func (x *ClassificationEvaluationMetrics) ProtoReflect() protoreflect.Message

func (*ClassificationEvaluationMetrics) Reset Uses

func (x *ClassificationEvaluationMetrics) Reset()

func (*ClassificationEvaluationMetrics) String Uses

func (x *ClassificationEvaluationMetrics) String() string

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry Uses

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct {

    // Output only. Metrics are computed with an assumption that the model
    // never returns predictions with score lower than this value.
    ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
    // Output only. Metrics are computed with an assumption that the model
    // always returns at most this many predictions (ordered by their score,
    // descendingly), but they all still need to meet the confidence_threshold.
    PositionThreshold int32 `protobuf:"varint,14,opt,name=position_threshold,json=positionThreshold,proto3" json:"position_threshold,omitempty"`
    // Output only. Recall (True Positive Rate) for the given confidence
    // threshold.
    Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
    // Output only. Precision for the given confidence threshold.
    Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`
    // Output only. False Positive Rate for the given confidence threshold.
    FalsePositiveRate float32 `protobuf:"fixed32,8,opt,name=false_positive_rate,json=falsePositiveRate,proto3" json:"false_positive_rate,omitempty"`
    // Output only. The harmonic mean of recall and precision.
    F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
    // Output only. The Recall (True Positive Rate) when only considering the
    // label that has the highest prediction score and not below the confidence
    // threshold for each example.
    RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"`
    // Output only. The precision when only considering the label that has the
    // highest prediction score and not below the confidence threshold for each
    // example.
    PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"`
    // Output only. The False Positive Rate when only considering the label that
    // has the highest prediction score and not below the confidence threshold
    // for each example.
    FalsePositiveRateAt1 float32 `protobuf:"fixed32,9,opt,name=false_positive_rate_at1,json=falsePositiveRateAt1,proto3" json:"false_positive_rate_at1,omitempty"`
    // Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
    F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"`
    // Output only. The number of model created labels that match a ground truth
    // label.
    TruePositiveCount int64 `protobuf:"varint,10,opt,name=true_positive_count,json=truePositiveCount,proto3" json:"true_positive_count,omitempty"`
    // Output only. The number of model created labels that do not match a
    // ground truth label.
    FalsePositiveCount int64 `protobuf:"varint,11,opt,name=false_positive_count,json=falsePositiveCount,proto3" json:"false_positive_count,omitempty"`
    // Output only. The number of ground truth labels that are not matched
    // by a model created label.
    FalseNegativeCount int64 `protobuf:"varint,12,opt,name=false_negative_count,json=falseNegativeCount,proto3" json:"false_negative_count,omitempty"`
    // Output only. The number of labels that were not created by the model,
    // but if they would, they would not match a ground truth label.
    TrueNegativeCount int64 `protobuf:"varint,13,opt,name=true_negative_count,json=trueNegativeCount,proto3" json:"true_negative_count,omitempty"`
    // contains filtered or unexported fields
}

Metrics for a single confidence threshold.

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor Uses

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

Deprecated: Use ClassificationEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1 Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1 Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold() int32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1 Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1 Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage Uses

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() string

type ClassificationEvaluationMetrics_ConfusionMatrix Uses

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 Uses

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

Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetRow Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage Uses

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect() protoreflect.Message

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Reset Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) Reset()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) String Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) String() string

type ClassificationEvaluationMetrics_ConfusionMatrix_Row Uses

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 Uses

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

Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix_Row.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage Uses

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect() protoreflect.Message

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) String Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() string

type ClassificationType Uses

type ClassificationType int32

Type of the classification problem.

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

func (ClassificationType) Descriptor Uses

func (ClassificationType) Descriptor() protoreflect.EnumDescriptor

func (ClassificationType) Enum Uses

func (x ClassificationType) Enum() *ClassificationType

func (ClassificationType) EnumDescriptor Uses

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

Deprecated: Use ClassificationType.Descriptor instead.

func (ClassificationType) Number Uses

func (x ClassificationType) Number() protoreflect.EnumNumber

func (ClassificationType) String Uses

func (x ClassificationType) String() string

func (ClassificationType) Type Uses

func (ClassificationType) Type() protoreflect.EnumType

type ColumnSpec Uses

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 Uses

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

Deprecated: Use ColumnSpec.ProtoReflect.Descriptor instead.

func (*ColumnSpec) GetDataStats Uses

func (x *ColumnSpec) GetDataStats() *DataStats

func (*ColumnSpec) GetDataType Uses

func (x *ColumnSpec) GetDataType() *DataType

func (*ColumnSpec) GetDisplayName Uses

func (x *ColumnSpec) GetDisplayName() string

func (*ColumnSpec) GetEtag Uses

func (x *ColumnSpec) GetEtag() string

func (*ColumnSpec) GetName Uses

func (x *ColumnSpec) GetName() string

func (*ColumnSpec) GetTopCorrelatedColumns Uses

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

func (*ColumnSpec) ProtoMessage Uses

func (*ColumnSpec) ProtoMessage()

func (*ColumnSpec) ProtoReflect Uses

func (x *ColumnSpec) ProtoReflect() protoreflect.Message

func (*ColumnSpec) Reset Uses

func (x *ColumnSpec) Reset()

func (*ColumnSpec) String Uses

func (x *ColumnSpec) String() string

type ColumnSpec_CorrelatedColumn Uses

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 Uses

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

Deprecated: Use ColumnSpec_CorrelatedColumn.ProtoReflect.Descriptor instead.

func (*ColumnSpec_CorrelatedColumn) GetColumnSpecId Uses

func (x *ColumnSpec_CorrelatedColumn) GetColumnSpecId() string

func (*ColumnSpec_CorrelatedColumn) GetCorrelationStats Uses

func (x *ColumnSpec_CorrelatedColumn) GetCorrelationStats() *CorrelationStats

func (*ColumnSpec_CorrelatedColumn) ProtoMessage Uses

func (*ColumnSpec_CorrelatedColumn) ProtoMessage()

func (*ColumnSpec_CorrelatedColumn) ProtoReflect Uses

func (x *ColumnSpec_CorrelatedColumn) ProtoReflect() protoreflect.Message

func (*ColumnSpec_CorrelatedColumn) Reset Uses

func (x *ColumnSpec_CorrelatedColumn) Reset()

func (*ColumnSpec_CorrelatedColumn) String Uses

func (x *ColumnSpec_CorrelatedColumn) String() string

type CorrelationStats Uses

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 Uses

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

Deprecated: Use CorrelationStats.ProtoReflect.Descriptor instead.

func (*CorrelationStats) GetCramersV Uses

func (x *CorrelationStats) GetCramersV() float64

func (*CorrelationStats) ProtoMessage Uses

func (*CorrelationStats) ProtoMessage()

func (*CorrelationStats) ProtoReflect Uses

func (x *CorrelationStats) ProtoReflect() protoreflect.Message

func (*CorrelationStats) Reset Uses

func (x *CorrelationStats) Reset()

func (*CorrelationStats) String Uses

func (x *CorrelationStats) String() string

type CreateDatasetRequest Uses

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 Uses

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

Deprecated: Use CreateDatasetRequest.ProtoReflect.Descriptor instead.

func (*CreateDatasetRequest) GetDataset Uses

func (x *CreateDatasetRequest) GetDataset() *Dataset

func (*CreateDatasetRequest) GetParent Uses

func (x *CreateDatasetRequest) GetParent() string

func (*CreateDatasetRequest) ProtoMessage Uses

func (*CreateDatasetRequest) ProtoMessage()

func (*CreateDatasetRequest) ProtoReflect Uses

func (x *CreateDatasetRequest) ProtoReflect() protoreflect.Message

func (*CreateDatasetRequest) Reset Uses

func (x *CreateDatasetRequest) Reset()

func (*CreateDatasetRequest) String Uses

func (x *CreateDatasetRequest) String() string

type CreateModelOperationMetadata Uses

type CreateModelOperationMetadata struct {
    // contains filtered or unexported fields
}

Details of CreateModel operation.

func (*CreateModelOperationMetadata) Descriptor Uses

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

Deprecated: Use CreateModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*CreateModelOperationMetadata) ProtoMessage Uses

func (*CreateModelOperationMetadata) ProtoMessage()

func (*CreateModelOperationMetadata) ProtoReflect Uses

func (x *CreateModelOperationMetadata) ProtoReflect() protoreflect.Message

func (*CreateModelOperationMetadata) Reset Uses

func (x *CreateModelOperationMetadata) Reset()

func (*CreateModelOperationMetadata) String Uses

func (x *CreateModelOperationMetadata) String() string

type CreateModelRequest Uses

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 Uses

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

Deprecated: Use CreateModelRequest.ProtoReflect.Descriptor instead.

func (*CreateModelRequest) GetModel Uses

func (x *CreateModelRequest) GetModel() *Model

func (*CreateModelRequest) GetParent Uses

func (x *CreateModelRequest) GetParent() string

func (*CreateModelRequest) ProtoMessage Uses

func (*CreateModelRequest) ProtoMessage()

func (*CreateModelRequest) ProtoReflect Uses

func (x *CreateModelRequest) ProtoReflect() protoreflect.Message

func (*CreateModelRequest) Reset Uses

func (x *CreateModelRequest) Reset()

func (*CreateModelRequest) String Uses

func (x *CreateModelRequest) String() string

type DataStats Uses

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 Uses

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

Deprecated: Use DataStats.ProtoReflect.Descriptor instead.

func (*DataStats) GetArrayStats Uses

func (x *DataStats) GetArrayStats() *ArrayStats

func (*DataStats) GetCategoryStats Uses

func (x *DataStats) GetCategoryStats() *CategoryStats

func (*DataStats) GetDistinctValueCount Uses

func (x *DataStats) GetDistinctValueCount() int64

func (*DataStats) GetFloat64Stats Uses

func (x *DataStats) GetFloat64Stats() *Float64Stats

func (*DataStats) GetNullValueCount Uses

func (x *DataStats) GetNullValueCount() int64

func (*DataStats) GetStats Uses

func (m *DataStats) GetStats() isDataStats_Stats

func (*DataStats) GetStringStats Uses

func (x *DataStats) GetStringStats() *StringStats

func (*DataStats) GetStructStats Uses

func (x *DataStats) GetStructStats() *StructStats

func (*DataStats) GetTimestampStats Uses

func (x *DataStats) GetTimestampStats() *TimestampStats

func (*DataStats) GetValidValueCount Uses

func (x *DataStats) GetValidValueCount() int64

func (*DataStats) ProtoMessage Uses

func (*DataStats) ProtoMessage()

func (*DataStats) ProtoReflect Uses

func (x *DataStats) ProtoReflect() protoreflect.Message

func (*DataStats) Reset Uses

func (x *DataStats) Reset()

func (*DataStats) String Uses

func (x *DataStats) String() string

type DataStats_ArrayStats Uses

type DataStats_ArrayStats struct {
    // The statistics for ARRAY DataType.
    ArrayStats *ArrayStats `protobuf:"bytes,6,opt,name=array_stats,json=arrayStats,proto3,oneof"`
}

type DataStats_CategoryStats Uses

type DataStats_CategoryStats struct {
    // The statistics for CATEGORY DataType.
    CategoryStats *CategoryStats `protobuf:"bytes,8,opt,name=category_stats,json=categoryStats,proto3,oneof"`
}

type DataStats_Float64Stats Uses

type DataStats_Float64Stats struct {
    // The statistics for FLOAT64 DataType.
    Float64Stats *Float64Stats `protobuf:"bytes,3,opt,name=float64_stats,json=float64Stats,proto3,oneof"`
}

type DataStats_StringStats Uses

type DataStats_StringStats struct {
    // The statistics for STRING DataType.
    StringStats *StringStats `protobuf:"bytes,4,opt,name=string_stats,json=stringStats,proto3,oneof"`
}

type DataStats_StructStats Uses

type DataStats_StructStats struct {
    // The statistics for STRUCT DataType.
    StructStats *StructStats `protobuf:"bytes,7,opt,name=struct_stats,json=structStats,proto3,oneof"`
}

type DataStats_TimestampStats Uses

type DataStats_TimestampStats struct {
    // The statistics for TIMESTAMP DataType.
    TimestampStats *TimestampStats `protobuf:"bytes,5,opt,name=timestamp_stats,json=timestampStats,proto3,oneof"`
}

type DataType Uses

type DataType struct {

    // Details of DataType-s that need additional specification.
    //
    // Types that are assignable to Details:
    //	*DataType_ListElementType
    //	*DataType_StructType
    //	*DataType_TimeFormat
    Details isDataType_Details `protobuf_oneof:"details"`
    // Required. The [TypeCode][google.cloud.automl.v1beta1.TypeCode] for this type.
    TypeCode TypeCode `protobuf:"varint,1,opt,name=type_code,json=typeCode,proto3,enum=google.cloud.automl.v1beta1.TypeCode" json:"type_code,omitempty"`
    // If true, this DataType can also be `NULL`. In .CSV files `NULL` value is
    // expressed as an empty string.
    Nullable bool `protobuf:"varint,4,opt,name=nullable,proto3" json:"nullable,omitempty"`
    // contains filtered or unexported fields
}

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

func (*DataType) Descriptor Uses

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

Deprecated: Use DataType.ProtoReflect.Descriptor instead.

func (*DataType) GetDetails Uses

func (m *DataType) GetDetails() isDataType_Details

func (*DataType) GetListElementType Uses

func (x *DataType) GetListElementType() *DataType

func (*DataType) GetNullable Uses

func (x *DataType) GetNullable() bool

func (*DataType) GetStructType Uses

func (x *DataType) GetStructType() *StructType

func (*DataType) GetTimeFormat Uses

func (x *DataType) GetTimeFormat() string

func (*DataType) GetTypeCode Uses

func (x *DataType) GetTypeCode() TypeCode

func (*DataType) ProtoMessage Uses

func (*DataType) ProtoMessage()

func (*DataType) ProtoReflect Uses

func (x *DataType) ProtoReflect() protoreflect.Message

func (*DataType) Reset Uses

func (x *DataType) Reset()

func (*DataType) String Uses

func (x *DataType) String() string

type DataType_ListElementType Uses

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"`
}

type DataType_StructType Uses

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"`
}

type DataType_TimeFormat Uses

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"`
}

type Dataset Uses

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 *timestamp.Timestamp `protobuf:"bytes,14,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
    // Used to perform consistent read-modify-write updates. If not set, a blind
    // "overwrite" update happens.
    Etag string `protobuf:"bytes,17,opt,name=etag,proto3" json:"etag,omitempty"`
    // 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 Uses

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

Deprecated: Use Dataset.ProtoReflect.Descriptor instead.

func (*Dataset) GetCreateTime Uses

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

func (*Dataset) GetDatasetMetadata Uses

func (m *Dataset) GetDatasetMetadata() isDataset_DatasetMetadata

func (*Dataset) GetDescription Uses

func (x *Dataset) GetDescription() string

func (*Dataset) GetDisplayName Uses

func (x *Dataset) GetDisplayName() string

func (*Dataset) GetEtag Uses

func (x *Dataset) GetEtag() string

func (*Dataset) GetExampleCount Uses

func (x *Dataset) GetExampleCount() int32

func (*Dataset) GetImageClassificationDatasetMetadata Uses

func (x *Dataset) GetImageClassificationDatasetMetadata() *ImageClassificationDatasetMetadata

func (*Dataset) GetImageObjectDetectionDatasetMetadata Uses

func (x *Dataset) GetImageObjectDetectionDatasetMetadata() *ImageObjectDetectionDatasetMetadata

func (*Dataset) GetName Uses

func (x *Dataset) GetName() string

func (*Dataset) GetTablesDatasetMetadata Uses

func (x *Dataset) GetTablesDatasetMetadata() *TablesDatasetMetadata

func (*Dataset) GetTextClassificationDatasetMetadata Uses

func (x *Dataset) GetTextClassificationDatasetMetadata() *TextClassificationDatasetMetadata

func (*Dataset) GetTextExtractionDatasetMetadata Uses

func (x *Dataset) GetTextExtractionDatasetMetadata() *TextExtractionDatasetMetadata

func (*Dataset) GetTextSentimentDatasetMetadata Uses

func (x *Dataset) GetTextSentimentDatasetMetadata() *TextSentimentDatasetMetadata

func (*Dataset) GetTranslationDatasetMetadata Uses

func (x *Dataset) GetTranslationDatasetMetadata() *TranslationDatasetMetadata

func (*Dataset) GetVideoClassificationDatasetMetadata Uses

func (x *Dataset) GetVideoClassificationDatasetMetadata() *VideoClassificationDatasetMetadata

func (*Dataset) GetVideoObjectTrackingDatasetMetadata Uses

func (x *Dataset) GetVideoObjectTrackingDatasetMetadata() *VideoObjectTrackingDatasetMetadata

func (*Dataset) ProtoMessage Uses

func (*Dataset) ProtoMessage()

func (*Dataset) ProtoReflect Uses

func (x *Dataset) ProtoReflect() protoreflect.Message

func (*Dataset) Reset Uses

func (x *Dataset) Reset()

func (*Dataset) String Uses

func (x *Dataset) String() string

type Dataset_ImageClassificationDatasetMetadata Uses

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"`
}

type Dataset_ImageObjectDetectionDatasetMetadata Uses

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"`
}

type Dataset_TablesDatasetMetadata Uses

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"`
}

type Dataset_TextClassificationDatasetMetadata Uses

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"`
}

type Dataset_TextExtractionDatasetMetadata Uses

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"`
}

type Dataset_TextSentimentDatasetMetadata Uses

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"`
}

type Dataset_TranslationDatasetMetadata Uses

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"`
}

type Dataset_VideoClassificationDatasetMetadata Uses

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"`
}

type Dataset_VideoObjectTrackingDatasetMetadata Uses

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"`
}

type DeleteDatasetRequest Uses

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 Uses

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

Deprecated: Use DeleteDatasetRequest.ProtoReflect.Descriptor instead.

func (*DeleteDatasetRequest) GetName Uses

func (x *DeleteDatasetRequest) GetName() string

func (*DeleteDatasetRequest) ProtoMessage Uses

func (*DeleteDatasetRequest) ProtoMessage()

func (*DeleteDatasetRequest) ProtoReflect Uses

func (x *DeleteDatasetRequest) ProtoReflect() protoreflect.Message

func (*DeleteDatasetRequest) Reset Uses

func (x *DeleteDatasetRequest) Reset()

func (*DeleteDatasetRequest) String Uses

func (x *DeleteDatasetRequest) String() string

type DeleteModelRequest Uses

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 Uses

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

Deprecated: Use DeleteModelRequest.ProtoReflect.Descriptor instead.

func (*DeleteModelRequest) GetName Uses

func (x *DeleteModelRequest) GetName() string

func (*DeleteModelRequest) ProtoMessage Uses

func (*DeleteModelRequest) ProtoMessage()

func (*DeleteModelRequest) ProtoReflect Uses

func (x *DeleteModelRequest) ProtoReflect() protoreflect.Message

func (*DeleteModelRequest) Reset Uses

func (x *DeleteModelRequest) Reset()

func (*DeleteModelRequest) String Uses

func (x *DeleteModelRequest) String() string

type DeleteOperationMetadata Uses

type DeleteOperationMetadata struct {
    // contains filtered or unexported fields
}

Details of operations that perform deletes of any entities.

func (*DeleteOperationMetadata) Descriptor Uses

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

Deprecated: Use DeleteOperationMetadata.ProtoReflect.Descriptor instead.

func (*DeleteOperationMetadata) ProtoMessage Uses

func (*DeleteOperationMetadata) ProtoMessage()

func (*DeleteOperationMetadata) ProtoReflect Uses

func (x *DeleteOperationMetadata) ProtoReflect() protoreflect.Message

func (*DeleteOperationMetadata) Reset Uses

func (x *DeleteOperationMetadata) Reset()

func (*DeleteOperationMetadata) String Uses

func (x *DeleteOperationMetadata) String() string

type DeployModelOperationMetadata Uses

type DeployModelOperationMetadata struct {
    // contains filtered or unexported fields
}

Details of DeployModel operation.

func (*DeployModelOperationMetadata) Descriptor Uses

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

Deprecated: Use DeployModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*DeployModelOperationMetadata) ProtoMessage Uses

func (*DeployModelOperationMetadata) ProtoMessage()

func (*DeployModelOperationMetadata) ProtoReflect Uses

func (x *DeployModelOperationMetadata) ProtoReflect() protoreflect.Message

func (*DeployModelOperationMetadata) Reset Uses

func (x *DeployModelOperationMetadata) Reset()

func (*DeployModelOperationMetadata) String Uses

func (x *DeployModelOperationMetadata) String() string

type DeployModelRequest Uses

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 Uses

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

Deprecated: Use DeployModelRequest.ProtoReflect.Descriptor instead.

func (*DeployModelRequest) GetImageClassificationModelDeploymentMetadata Uses

func (x *DeployModelRequest) GetImageClassificationModelDeploymentMetadata() *ImageClassificationModelDeploymentMetadata

func (*DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata Uses

func (x *DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata() *ImageObjectDetectionModelDeploymentMetadata

func (*DeployModelRequest) GetModelDeploymentMetadata Uses

func (m *DeployModelRequest) GetModelDeploymentMetadata() isDeployModelRequest_ModelDeploymentMetadata

func (*DeployModelRequest) GetName Uses

func (x *DeployModelRequest) GetName() string

func (*DeployModelRequest) ProtoMessage Uses

func (*DeployModelRequest) ProtoMessage()

func (*DeployModelRequest) ProtoReflect Uses

func (x *DeployModelRequest) ProtoReflect() protoreflect.Message

func (*DeployModelRequest) Reset Uses

func (x *DeployModelRequest) Reset()

func (*DeployModelRequest) String Uses

func (x *DeployModelRequest) String() string

type DeployModelRequest_ImageClassificationModelDeploymentMetadata Uses

type DeployModelRequest_ImageClassificationModelDeploymentMetadata struct {
    // Model deployment metadata specific to Image Classification.
    ImageClassificationModelDeploymentMetadata *ImageClassificationModelDeploymentMetadata `protobuf:"bytes,4,opt,name=image_classification_model_deployment_metadata,json=imageClassificationModelDeploymentMetadata,proto3,oneof"`
}

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata Uses

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata struct {
    // Model deployment metadata specific to Image Object Detection.
    ImageObjectDetectionModelDeploymentMetadata *ImageObjectDetectionModelDeploymentMetadata `protobuf:"bytes,2,opt,name=image_object_detection_model_deployment_metadata,json=imageObjectDetectionModelDeploymentMetadata,proto3,oneof"`
}

type Document Uses

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 Uses

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

Deprecated: Use Document.ProtoReflect.Descriptor instead.

func (*Document) GetDocumentDimensions Uses

func (x *Document) GetDocumentDimensions() *DocumentDimensions

func (*Document) GetDocumentText Uses

func (x *Document) GetDocumentText() *TextSnippet

func (*Document) GetInputConfig Uses

func (x *Document) GetInputConfig() *DocumentInputConfig

func (*Document) GetLayout Uses

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

func (*Document) GetPageCount Uses

func (x *Document) GetPageCount() int32

func (*Document) ProtoMessage Uses

func (*Document) ProtoMessage()

func (*Document) ProtoReflect Uses

func (x *Document) ProtoReflect() protoreflect.Message

func (*Document) Reset Uses

func (x *Document) Reset()

func (*Document) String Uses

func (x *Document) String() string

type DocumentDimensions Uses

type DocumentDimensions struct {

    // Unit of the dimension.
    Unit DocumentDimensions_DocumentDimensionUnit `protobuf:"varint,1,opt,name=unit,proto3,enum=google.cloud.automl.v1beta1.DocumentDimensions_DocumentDimensionUnit" json:"unit,omitempty"`
    // Width value of the document, works together with the unit.
    Width float32 `protobuf:"fixed32,2,opt,name=width,proto3" json:"width,omitempty"`
    // Height value of the document, works together with the unit.
    Height float32 `protobuf:"fixed32,3,opt,name=height,proto3" json:"height,omitempty"`
    // contains filtered or unexported fields
}

Message that describes dimension of a document.

func (*DocumentDimensions) Descriptor Uses

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

Deprecated: Use DocumentDimensions.ProtoReflect.Descriptor instead.

func (*DocumentDimensions) GetHeight Uses

func (x *DocumentDimensions) GetHeight() float32

func (*DocumentDimensions) GetUnit Uses

func (x *DocumentDimensions) GetUnit() DocumentDimensions_DocumentDimensionUnit

func (*DocumentDimensions) GetWidth Uses

func (x *DocumentDimensions) GetWidth() float32

func (*DocumentDimensions) ProtoMessage Uses

func (*DocumentDimensions) ProtoMessage()

func (*DocumentDimensions) ProtoReflect Uses

func (x *DocumentDimensions) ProtoReflect() protoreflect.Message

func (*DocumentDimensions) Reset Uses

func (x *DocumentDimensions) Reset()

func (*DocumentDimensions) String Uses

func (x *DocumentDimensions) String() string

type DocumentDimensions_DocumentDimensionUnit Uses

type DocumentDimensions_DocumentDimensionUnit int32

Unit of the document dimension.

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

func (DocumentDimensions_DocumentDimensionUnit) Descriptor Uses

func (DocumentDimensions_DocumentDimensionUnit) Descriptor() protoreflect.EnumDescriptor

func (DocumentDimensions_DocumentDimensionUnit) Enum Uses

func (x DocumentDimensions_DocumentDimensionUnit) Enum() *DocumentDimensions_DocumentDimensionUnit

func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor Uses

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

Deprecated: Use DocumentDimensions_DocumentDimensionUnit.Descriptor instead.

func (DocumentDimensions_DocumentDimensionUnit) Number Uses

func (x DocumentDimensions_DocumentDimensionUnit) Number() protoreflect.EnumNumber

func (DocumentDimensions_DocumentDimensionUnit) String Uses

func (x DocumentDimensions_DocumentDimensionUnit) String() string

func (DocumentDimensions_DocumentDimensionUnit) Type Uses

func (DocumentDimensions_DocumentDimensionUnit) Type() protoreflect.EnumType

type DocumentInputConfig Uses

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 Uses

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

Deprecated: Use DocumentInputConfig.ProtoReflect.Descriptor instead.

func (*DocumentInputConfig) GetGcsSource Uses

func (x *DocumentInputConfig) GetGcsSource() *GcsSource

func (*DocumentInputConfig) ProtoMessage Uses

func (*DocumentInputConfig) ProtoMessage()

func (*DocumentInputConfig) ProtoReflect Uses

func (x *DocumentInputConfig) ProtoReflect() protoreflect.Message

func (*DocumentInputConfig) Reset Uses

func (x *DocumentInputConfig) Reset()

func (*DocumentInputConfig) String Uses

func (x *DocumentInputConfig) String() string

type Document_Layout Uses

type Document_Layout struct {

    // Text Segment that represents a segment in
    // [document_text][google.cloud.automl.v1beta1.Document.document_text].
    TextSegment *TextSegment `protobuf:"bytes,1,opt,name=text_segment,json=textSegment,proto3" json:"text_segment,omitempty"`
    // Page number of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the original document, starts
    // from 1.
    PageNumber int32 `protobuf:"varint,2,opt,name=page_number,json=pageNumber,proto3" json:"page_number,omitempty"`
    // The position of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the page.
    // Contains exactly 4
    //
    // [normalized_vertices][google.cloud.automl.v1beta1.BoundingPoly.normalized_vertices]
    // and they are connected by edges in the order provided, which will
    // represent a rectangle parallel to the frame. The
    // [NormalizedVertex-s][google.cloud.automl.v1beta1.NormalizedVertex] are
    // relative to the page.
    // Coordinates are based on top-left as point (0,0).
    BoundingPoly *BoundingPoly `protobuf:"bytes,3,opt,name=bounding_poly,json=boundingPoly,proto3" json:"bounding_poly,omitempty"`
    // The type of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in document.
    TextSegmentType Document_Layout_TextSegmentType `protobuf:"varint,4,opt,name=text_segment_type,json=textSegmentType,proto3,enum=google.cloud.automl.v1beta1.Document_Layout_TextSegmentType" json:"text_segment_type,omitempty"`
    // contains filtered or unexported fields
}

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

func (*Document_Layout) Descriptor Uses

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

Deprecated: Use Document_Layout.ProtoReflect.Descriptor instead.

func (*Document_Layout) GetBoundingPoly Uses

func (x *Document_Layout) GetBoundingPoly() *BoundingPoly

func (*Document_Layout) GetPageNumber Uses

func (x *Document_Layout) GetPageNumber() int32

func (*Document_Layout) GetTextSegment Uses

func (x *Document_Layout) GetTextSegment() *TextSegment

func (*Document_Layout) GetTextSegmentType Uses

func (x *Document_Layout) GetTextSegmentType() Document_Layout_TextSegmentType

func (*Document_Layout) ProtoMessage Uses

func (*Document_Layout) ProtoMessage()

func (*Document_Layout) ProtoReflect Uses

func (x *Document_Layout) ProtoReflect() protoreflect.Message

func (*Document_Layout) Reset Uses

func (x *Document_Layout) Reset()

func (*Document_Layout) String Uses

func (x *Document_Layout) String() string

type Document_Layout_TextSegmentType Uses

type Document_Layout_TextSegmentType int32

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

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

func (Document_Layout_TextSegmentType) Descriptor Uses

func (Document_Layout_TextSegmentType) Descriptor() protoreflect.EnumDescriptor

func (Document_Layout_TextSegmentType) Enum Uses

func (x Document_Layout_TextSegmentType) Enum() *Document_Layout_TextSegmentType

func (Document_Layout_TextSegmentType) EnumDescriptor Uses

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

Deprecated: Use Document_Layout_TextSegmentType.Descriptor instead.

func (Document_Layout_TextSegmentType) Number Uses

func (x Document_Layout_TextSegmentType) Number() protoreflect.EnumNumber

func (Document_Layout_TextSegmentType) String Uses

func (x Document_Layout_TextSegmentType) String() string

func (Document_Layout_TextSegmentType) Type Uses

func (Document_Layout_TextSegmentType) Type() protoreflect.EnumType

type DoubleRange Uses

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 Uses

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

Deprecated: Use DoubleRange.ProtoReflect.Descriptor instead.

func (*DoubleRange) GetEnd Uses

func (x *DoubleRange) GetEnd() float64

func (*DoubleRange) GetStart Uses

func (x *DoubleRange) GetStart() float64

func (*DoubleRange) ProtoMessage Uses

func (*DoubleRange) ProtoMessage()

func (*DoubleRange) ProtoReflect Uses

func (x *DoubleRange) ProtoReflect() protoreflect.Message

func (*DoubleRange) Reset Uses

func (x *DoubleRange) Reset()

func (*DoubleRange) String Uses

func (x *DoubleRange) String() string

type ExamplePayload Uses

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 Uses

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

Deprecated: Use ExamplePayload.ProtoReflect.Descriptor instead.

func (*ExamplePayload) GetDocument Uses

func (x *ExamplePayload) GetDocument() *Document

func (*ExamplePayload) GetImage Uses

func (x *ExamplePayload) GetImage() *Image

func (*ExamplePayload) GetPayload Uses

func (m *ExamplePayload) GetPayload() isExamplePayload_Payload

func (*ExamplePayload) GetRow Uses

func (x *ExamplePayload) GetRow() *Row

func (*ExamplePayload) GetTextSnippet Uses

func (x *ExamplePayload) GetTextSnippet() *TextSnippet

func (*ExamplePayload) ProtoMessage Uses

func (*ExamplePayload) ProtoMessage()

func (*ExamplePayload) ProtoReflect Uses

func (x *ExamplePayload) ProtoReflect() protoreflect.Message

func (*ExamplePayload) Reset Uses

func (x *ExamplePayload) Reset()

func (*ExamplePayload) String Uses

func (x *ExamplePayload) String() string

type ExamplePayload_Document Uses

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

type ExamplePayload_Image Uses

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

type ExamplePayload_Row Uses

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

type ExamplePayload_TextSnippet Uses

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

type ExportDataOperationMetadata Uses

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 Uses

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

Deprecated: Use ExportDataOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportDataOperationMetadata) GetOutputInfo Uses

func (x *ExportDataOperationMetadata) GetOutputInfo() *ExportDataOperationMetadata_ExportDataOutputInfo

func (*ExportDataOperationMetadata) ProtoMessage Uses

func (*ExportDataOperationMetadata) ProtoMessage()

func (*ExportDataOperationMetadata) ProtoReflect Uses

func (x *ExportDataOperationMetadata) ProtoReflect() protoreflect.Message

func (*ExportDataOperationMetadata) Reset Uses

func (x *ExportDataOperationMetadata) Reset()

func (*ExportDataOperationMetadata) String Uses

func (x *ExportDataOperationMetadata) String() string

type ExportDataOperationMetadata_ExportDataOutputInfo Uses

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 Uses

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

Deprecated: Use ExportDataOperationMetadata_ExportDataOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetBigqueryOutputDataset Uses

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) GetBigqueryOutputDataset() string

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory Uses

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory() string

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation Uses

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation() isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage Uses

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoReflect Uses

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) ProtoReflect() protoreflect.Message

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Reset Uses

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) Reset()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) String Uses

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) String() string

type ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset Uses

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"`
}

type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory Uses

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"`
}

type ExportDataRequest Uses

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 Uses

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

Deprecated: Use ExportDataRequest.ProtoReflect.Descriptor instead.

func (*ExportDataRequest) GetName Uses

func (x *ExportDataRequest) GetName() string

func (*ExportDataRequest) GetOutputConfig Uses

func (x *ExportDataRequest) GetOutputConfig() *OutputConfig

func (*ExportDataRequest) ProtoMessage Uses

func (*ExportDataRequest) ProtoMessage()

func (*ExportDataRequest) ProtoReflect Uses

func (x *ExportDataRequest) ProtoReflect() protoreflect.Message

func (*ExportDataRequest) Reset Uses

func (x *ExportDataRequest) Reset()

func (*ExportDataRequest) String Uses

func (x *ExportDataRequest) String() string

type ExportEvaluatedExamplesOperationMetadata Uses

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 Uses

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

Deprecated: Use ExportEvaluatedExamplesOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportEvaluatedExamplesOperationMetadata) GetOutputInfo Uses

func (x *ExportEvaluatedExamplesOperationMetadata) GetOutputInfo() *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo

func (*ExportEvaluatedExamplesOperationMetadata) ProtoMessage Uses

func (*ExportEvaluatedExamplesOperationMetadata) ProtoMessage()

func (*ExportEvaluatedExamplesOperationMetadata) ProtoReflect Uses

func (x *ExportEvaluatedExamplesOperationMetadata) ProtoReflect() protoreflect.Message

func (*ExportEvaluatedExamplesOperationMetadata) Reset Uses

func (x *ExportEvaluatedExamplesOperationMetadata) Reset()

func (*ExportEvaluatedExamplesOperationMetadata) String Uses

func (x *ExportEvaluatedExamplesOperationMetadata) String() string

type ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo Uses

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 Uses

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

Deprecated: Use ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) GetBigqueryOutputDataset Uses

func (x *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) GetBigqueryOutputDataset() string

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoMessage Uses

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoMessage()

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoReflect Uses

func (x *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) ProtoReflect() protoreflect.Message

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) Reset Uses

func (x *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) Reset()

func (*ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) String Uses

func (x *ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo) String() string

type ExportEvaluatedExamplesOutputConfig Uses

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 Uses

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

Deprecated: Use ExportEvaluatedExamplesOutputConfig.ProtoReflect.Descriptor instead.

func (*ExportEvaluatedExamplesOutputConfig) GetBigqueryDestination Uses

func (x *ExportEvaluatedExamplesOutputConfig) GetBigqueryDestination() *BigQueryDestination

func (*ExportEvaluatedExamplesOutputConfig) GetDestination Uses

func (m *ExportEvaluatedExamplesOutputConfig) GetDestination() isExportEvaluatedExamplesOutputConfig_Destination

func (*ExportEvaluatedExamplesOutputConfig) ProtoMessage Uses

func (*ExportEvaluatedExamplesOutputConfig) ProtoMessage()

func (*ExportEvaluatedExamplesOutputConfig) ProtoReflect Uses

func (x *ExportEvaluatedExamplesOutputConfig) ProtoReflect() protoreflect.Message

func (*ExportEvaluatedExamplesOutputConfig) Reset Uses

func (x *ExportEvaluatedExamplesOutputConfig) Reset()

func (*ExportEvaluatedExamplesOutputConfig) String Uses

func (x *ExportEvaluatedExamplesOutputConfig) String() string

type ExportEvaluatedExamplesOutputConfig_BigqueryDestination Uses

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"`
}

type ExportEvaluatedExamplesRequest Uses

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 Uses

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

Deprecated: Use ExportEvaluatedExamplesRequest.ProtoReflect.Descriptor instead.

func (*ExportEvaluatedExamplesRequest) GetName Uses

func (x *ExportEvaluatedExamplesRequest) GetName() string

func (*ExportEvaluatedExamplesRequest) GetOutputConfig Uses

func (x *ExportEvaluatedExamplesRequest) GetOutputConfig() *ExportEvaluatedExamplesOutputConfig

func (*ExportEvaluatedExamplesRequest) ProtoMessage Uses

func (*ExportEvaluatedExamplesRequest) ProtoMessage()

func (*ExportEvaluatedExamplesRequest) ProtoReflect Uses

func (x *ExportEvaluatedExamplesRequest) ProtoReflect() protoreflect.Message

func (*ExportEvaluatedExamplesRequest) Reset Uses

func (x *ExportEvaluatedExamplesRequest) Reset()

func (*ExportEvaluatedExamplesRequest) String Uses

func (x *ExportEvaluatedExamplesRequest) String() string

type ExportModelOperationMetadata Uses

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 Uses

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

Deprecated: Use ExportModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportModelOperationMetadata) GetOutputInfo Uses

func (x *ExportModelOperationMetadata) GetOutputInfo() *ExportModelOperationMetadata_ExportModelOutputInfo

func (*ExportModelOperationMetadata) ProtoMessage Uses

func (*ExportModelOperationMetadata) ProtoMessage()

func (*ExportModelOperationMetadata) ProtoReflect Uses

func (x *ExportModelOperationMetadata) ProtoReflect() protoreflect.Message

func (*ExportModelOperationMetadata) Reset Uses

func (x *ExportModelOperationMetadata) Reset()

func (*ExportModelOperationMetadata) String Uses

func (x *ExportModelOperationMetadata) String() string

type ExportModelOperationMetadata_ExportModelOutputInfo Uses

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 Uses

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

Deprecated: Use ExportModelOperationMetadata_ExportModelOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory Uses

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory() string

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage Uses

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoReflect Uses

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) ProtoReflect() protoreflect.Message

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Reset Uses

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) Reset()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) String Uses

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) String() string

type ExportModelRequest Uses

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 Uses

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

Deprecated: Use ExportModelRequest.ProtoReflect.Descriptor instead.

func (*ExportModelRequest) GetName Uses

func (x *ExportModelRequest) GetName() string

func (*ExportModelRequest) GetOutputConfig Uses

func (x *ExportModelRequest) GetOutputConfig() *ModelExportOutputConfig

func (*ExportModelRequest) ProtoMessage Uses

func (*ExportModelRequest) ProtoMessage()

func (*ExportModelRequest) ProtoReflect Uses

func (x *ExportModelRequest) ProtoReflect() protoreflect.Message

func (*ExportModelRequest) Reset Uses

func (x *ExportModelRequest) Reset()

func (*ExportModelRequest) String Uses

func (x *ExportModelRequest) String() string

type Float64Stats Uses

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 Uses

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

Deprecated: Use Float64Stats.ProtoReflect.Descriptor instead.

func (*Float64Stats) GetHistogramBuckets Uses

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

func (*Float64Stats) GetMean Uses

func (x *Float64Stats) GetMean() float64

func (*Float64Stats) GetQuantiles Uses

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

func (*Float64Stats) GetStandardDeviation Uses

func (x *Float64Stats) GetStandardDeviation() float64

func (*Float64Stats) ProtoMessage Uses

func (*Float64Stats) ProtoMessage()

func (*Float64Stats) ProtoReflect Uses

func (x *Float64Stats) ProtoReflect() protoreflect.Message

func (*Float64Stats) Reset Uses

func (x *Float64Stats) Reset()

func (*Float64Stats) String Uses

func (x *Float64Stats) String() string

type Float64Stats_HistogramBucket Uses

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 Uses

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

Deprecated: Use Float64Stats_HistogramBucket.ProtoReflect.Descriptor instead.

func (*Float64Stats_HistogramBucket) GetCount Uses

func (x *Float64Stats_HistogramBucket) GetCount() int64

func (*Float64Stats_HistogramBucket) GetMax Uses

func (x *Float64Stats_HistogramBucket) GetMax() float64

func (*Float64Stats_HistogramBucket) GetMin Uses

func (x *Float64Stats_HistogramBucket) GetMin() float64

func (*Float64Stats_HistogramBucket) ProtoMessage Uses

func (*Float64Stats_HistogramBucket) ProtoMessage()

func (*Float64Stats_HistogramBucket) ProtoReflect Uses

func (x *Float64Stats_HistogramBucket) ProtoReflect() protoreflect.Message

func (*Float64Stats_HistogramBucket) Reset Uses

func (x *Float64Stats_HistogramBucket) Reset()

func (*Float64Stats_HistogramBucket) String Uses

func (x *Float64Stats_HistogramBucket) String() string

type GcrDestination Uses

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 Uses

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

Deprecated: Use GcrDestination.ProtoReflect.Descriptor instead.

func (*GcrDestination) GetOutputUri Uses

func (x *GcrDestination) GetOutputUri() string

func (*GcrDestination) ProtoMessage Uses

func (*GcrDestination) ProtoMessage()

func (*GcrDestination) ProtoReflect Uses

func (x *GcrDestination) ProtoReflect() protoreflect.Message

func (*GcrDestination) Reset Uses

func (x *GcrDestination) Reset()

func (*GcrDestination) String Uses

func (x *GcrDestination) String() string

type GcsDestination Uses

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 Uses

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

Deprecated: Use GcsDestination.ProtoReflect.Descriptor instead.

func (*GcsDestination) GetOutputUriPrefix Uses

func (x *GcsDestination) GetOutputUriPrefix() string

func (*GcsDestination) ProtoMessage Uses

func (*GcsDestination) ProtoMessage()

func (*GcsDestination) ProtoReflect Uses

func (x *GcsDestination) ProtoReflect() protoreflect.Message

func (*GcsDestination) Reset Uses

func (x *GcsDestination) Reset()

func (*GcsDestination) String Uses

func (x *GcsDestination) String() string

type GcsSource Uses

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 Uses

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

Deprecated: Use GcsSource.ProtoReflect.Descriptor instead.

func (*GcsSource) GetInputUris Uses

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

func (*GcsSource) ProtoMessage Uses

func (*GcsSource) ProtoMessage()

func (*GcsSource) ProtoReflect Uses

func (x *GcsSource) ProtoReflect() protoreflect.Message

func (*GcsSource) Reset Uses

func (x *GcsSource) Reset()

func (*GcsSource) String Uses

func (x *GcsSource) String() string

type GetAnnotationSpecRequest Uses

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 Uses

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

Deprecated: Use GetAnnotationSpecRequest.ProtoReflect.Descriptor instead.

func (*GetAnnotationSpecRequest) GetName Uses

func (x *GetAnnotationSpecRequest) GetName() string

func (*GetAnnotationSpecRequest) ProtoMessage Uses

func (*GetAnnotationSpecRequest) ProtoMessage()

func (*GetAnnotationSpecRequest) ProtoReflect Uses

func (x *GetAnnotationSpecRequest) ProtoReflect() protoreflect.Message

func (*GetAnnotationSpecRequest) Reset Uses

func (x *GetAnnotationSpecRequest) Reset()

func (*GetAnnotationSpecRequest) String Uses

func (x *GetAnnotationSpecRequest) String() string

type GetColumnSpecRequest Uses

type GetColumnSpecRequest struct {

    // Required. The resource name of the column spec to retrieve.
    Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
    // Mask specifying which fields to read.
    FieldMask *field_mask.FieldMask `protobuf:"bytes,2,opt,name=field_mask,json=fieldMask,proto3" json:"field_mask,omitempty"`
    // contains filtered or unexported fields
}

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

func (*GetColumnSpecRequest) Descriptor Uses

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

Deprecated: Use GetColumnSpecRequest.ProtoReflect.Descriptor instead.

func (*GetColumnSpecRequest) GetFieldMask Uses

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

func (*GetColumnSpecRequest) GetName Uses

func (x *GetColumnSpecRequest) GetName() string

func (*GetColumnSpecRequest) ProtoMessage Uses

func (*GetColumnSpecRequest) ProtoMessage()

func (*GetColumnSpecRequest) ProtoReflect Uses

func (x *GetColumnSpecRequest) ProtoReflect() protoreflect.Message

func (*GetColumnSpecRequest) Reset Uses

func (x *GetColumnSpecRequest) Reset()

func (*GetColumnSpecRequest) String Uses

func (x *GetColumnSpecRequest) String() string

type GetDatasetRequest Uses

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 Uses

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

Deprecated: Use GetDatasetRequest.ProtoReflect.Descriptor instead.

func (*GetDatasetRequest) GetName Uses

func (x *GetDatasetRequest) GetName() string

func (*GetDatasetRequest) ProtoMessage Uses

func (*GetDatasetRequest) ProtoMessage()

func (*GetDatasetRequest) ProtoReflect Uses

func (x *GetDatasetRequest) ProtoReflect() protoreflect.Message

func (*GetDatasetRequest) Reset Uses

func (x *GetDatasetRequest) Reset()

func (*GetDatasetRequest) String Uses

func (x *GetDatasetRequest) String() string

type GetModelEvaluationRequest Uses

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 Uses

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

Deprecated: Use GetModelEvaluationRequest.ProtoReflect.Descriptor instead.

func (*GetModelEvaluationRequest) GetName Uses

func (x *GetModelEvaluationRequest) GetName() string

func (*GetModelEvaluationRequest) ProtoMessage Uses

func (*GetModelEvaluationRequest) ProtoMessage()

func (*GetModelEvaluationRequest) ProtoReflect Uses

func (x *GetModelEvaluationRequest) ProtoReflect() protoreflect.Message

func (*GetModelEvaluationRequest) Reset Uses

func (x *GetModelEvaluationRequest) Reset()

func (*GetModelEvaluationRequest) String Uses

func (x *GetModelEvaluationRequest) String() string

type GetModelRequest Uses

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 Uses

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

Deprecated: Use GetModelRequest.ProtoReflect.Descriptor instead.

func (*GetModelRequest) GetName Uses

func (x *GetModelRequest) GetName() string

func (*GetModelRequest) ProtoMessage Uses

func (*GetModelRequest) ProtoMessage()

func (*GetModelRequest) ProtoReflect Uses

func (x *GetModelRequest) ProtoReflect() protoreflect.Message

func (*GetModelRequest) Reset Uses

func (x *GetModelRequest) Reset()

func (*GetModelRequest) String Uses

func (x *GetModelRequest) String() string

type GetTableSpecRequest Uses

type GetTableSpecRequest struct {

    // Required. The resource name of the table spec to retrieve.
    Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
    // Mask specifying which fields to read.
    FieldMask *field_mask.FieldMask `protobuf:"bytes,2,opt,name=field_mask,json=fieldMask,proto3" json:"field_mask,omitempty"`
    // contains filtered or unexported fields
}

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

func (*GetTableSpecRequest) Descriptor Uses

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

Deprecated: Use GetTableSpecRequest.ProtoReflect.Descriptor instead.

func (*GetTableSpecRequest) GetFieldMask Uses

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

func (*GetTableSpecRequest) GetName Uses

func (x *GetTableSpecRequest) GetName() string

func (*GetTableSpecRequest) ProtoMessage Uses

func (*GetTableSpecRequest) ProtoMessage()

func (*GetTableSpecRequest) ProtoReflect Uses

func (x *GetTableSpecRequest) ProtoReflect() protoreflect.Message

func (*GetTableSpecRequest) Reset Uses

func (x *GetTableSpecRequest) Reset()

func (*GetTableSpecRequest) String Uses

func (x *GetTableSpecRequest) String() string

type Image Uses

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 Uses

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

Deprecated: Use Image.ProtoReflect.Descriptor instead.

func (*Image) GetData Uses

func (m *Image) GetData() isImage_Data

func (*Image) GetImageBytes Uses

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

func (*Image) GetInputConfig Uses

func (x *Image) GetInputConfig() *InputConfig

func (*Image) GetThumbnailUri Uses

func (x *Image) GetThumbnailUri() string

func (*Image) ProtoMessage Uses

func (*Image) ProtoMessage()

func (*Image) ProtoReflect Uses

func (x *Image) ProtoReflect() protoreflect.Message

func (*Image) Reset Uses

func (x *Image) Reset()

func (*Image) String Uses

func (x *Image) String() string

type ImageClassificationDatasetMetadata Uses

type ImageClassificationDatasetMetadata struct {

    // Required. Type of the classification problem.
    ClassificationType ClassificationType `protobuf:"varint,1,opt,name=classification_type,json=classificationType,proto3,enum=google.cloud.automl.v1beta1.ClassificationType" json:"classification_type,omitempty"`
    // contains filtered or unexported fields
}

Dataset metadata that is specific to image classification.

func (*ImageClassificationDatasetMetadata) Descriptor Uses

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

Deprecated: Use ImageClassificationDatasetMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationDatasetMetadata) GetClassificationType Uses

func (x *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationType

func (*ImageClassificationDatasetMetadata) ProtoMessage Uses

func (*ImageClassificationDatasetMetadata) ProtoMessage()

func (*ImageClassificationDatasetMetadata) ProtoReflect Uses

func (x *ImageClassificationDatasetMetadata) ProtoReflect() protoreflect.Message

func (*ImageClassificationDatasetMetadata) Reset Uses

func (x *ImageClassificationDatasetMetadata) Reset()

func (*ImageClassificationDatasetMetadata) String Uses

func (x *ImageClassificationDatasetMetadata) String() string

type ImageClassificationModelDeploymentMetadata Uses

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 Uses

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

Deprecated: Use ImageClassificationModelDeploymentMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationModelDeploymentMetadata) GetNodeCount Uses

func (x *ImageClassificationModelDeploymentMetadata) GetNodeCount() int64

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage Uses

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage()

func (*ImageClassificationModelDeploymentMetadata) ProtoReflect Uses

func (x *ImageClassificationModelDeploymentMetadata) ProtoReflect() protoreflect.Message

func (*ImageClassificationModelDeploymentMetadata) Reset Uses

func (x *ImageClassificationModelDeploymentMetadata) Reset()

func (*ImageClassificationModelDeploymentMetadata) String Uses

func (x *ImageClassificationModelDeploymentMetadata) String() string

type ImageClassificationModelMetadata Uses

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 Uses

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

Deprecated: Use ImageClassificationModelMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationModelMetadata) GetBaseModelId Uses

func (x *ImageClassificationModelMetadata) GetBaseModelId() string

func (*ImageClassificationModelMetadata) GetModelType Uses

func (x *ImageClassificationModelMetadata) GetModelType() string

func (*ImageClassificationModelMetadata) GetNodeCount Uses

func (x *ImageClassificationModelMetadata) GetNodeCount() int64

func (*ImageClassificationModelMetadata) GetNodeQps Uses

func (x *ImageClassificationModelMetadata) GetNodeQps() float64

func (*ImageClassificationModelMetadata) GetStopReason Uses

func (x *ImageClassificationModelMetadata) GetStopReason() string

func (*ImageClassificationModelMetadata) GetTrainBudget Uses

func (x *ImageClassificationModelMetadata) GetTrainBudget() int64

func (*ImageClassificationModelMetadata) GetTrainCost Uses

func (x *ImageClassificationModelMetadata) GetTrainCost() int64

func (*ImageClassificationModelMetadata) ProtoMessage Uses

func (*ImageClassificationModelMetadata) ProtoMessage()

func (*ImageClassificationModelMetadata) ProtoReflect Uses

func (x *ImageClassificationModelMetadata) ProtoReflect() protoreflect.Message

func (*ImageClassificationModelMetadata) Reset Uses

func (x *ImageClassificationModelMetadata) Reset()

func (*ImageClassificationModelMetadata) String Uses

func (x *ImageClassificationModelMetadata) String() string

type ImageObjectDetectionAnnotation Uses

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 Uses

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

Deprecated: Use ImageObjectDetectionAnnotation.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionAnnotation) GetBoundingBox Uses

func (x *ImageObjectDetectionAnnotation) GetBoundingBox() *BoundingPoly

func (*ImageObjectDetectionAnnotation) GetScore Uses

func (x *ImageObjectDetectionAnnotation) GetScore() float32

func (*ImageObjectDetectionAnnotation) ProtoMessage Uses

func (*ImageObjectDetectionAnnotation) ProtoMessage()

func (*ImageObjectDetectionAnnotation) ProtoReflect Uses

func (x *ImageObjectDetectionAnnotation) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionAnnotation) Reset Uses

func (x *ImageObjectDetectionAnnotation) Reset()

func (*ImageObjectDetectionAnnotation) String Uses

func (x *ImageObjectDetectionAnnotation) String() string

type ImageObjectDetectionDatasetMetadata Uses

type ImageObjectDetectionDatasetMetadata struct {
    // contains filtered or unexported fields
}

Dataset metadata specific to image object detection.

func (*ImageObjectDetectionDatasetMetadata) Descriptor Uses

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

Deprecated: Use ImageObjectDetectionDatasetMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage Uses

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage()

func (*ImageObjectDetectionDatasetMetadata) ProtoReflect Uses

func (x *ImageObjectDetectionDatasetMetadata) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionDatasetMetadata) Reset Uses

func (x *ImageObjectDetectionDatasetMetadata) Reset()

func (*ImageObjectDetectionDatasetMetadata) String Uses

func (x *ImageObjectDetectionDatasetMetadata) String() string

type ImageObjectDetectionEvaluationMetrics Uses

type ImageObjectDetectionEvaluationMetrics struct {

    // Output only. The total number of bounding boxes (i.e. summed over all
    // images) the ground truth used to create this evaluation had.
    EvaluatedBoundingBoxCount int32 `protobuf:"varint,1,opt,name=evaluated_bounding_box_count,json=evaluatedBoundingBoxCount,proto3" json:"evaluated_bounding_box_count,omitempty"`
    // Output only. The bounding boxes match metrics for each
    // Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
    // and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
    // pair.
    BoundingBoxMetricsEntries []*BoundingBoxMetricsEntry `protobuf:"bytes,2,rep,name=bounding_box_metrics_entries,json=boundingBoxMetricsEntries,proto3" json:"bounding_box_metrics_entries,omitempty"`
    // Output only. The single metric for bounding boxes evaluation:
    // the mean_average_precision averaged over all bounding_box_metrics_entries.
    BoundingBoxMeanAveragePrecision float32 `protobuf:"fixed32,3,opt,name=bounding_box_mean_average_precision,json=boundingBoxMeanAveragePrecision,proto3" json:"bounding_box_mean_average_precision,omitempty"`
    // contains filtered or unexported fields
}

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

func (*ImageObjectDetectionEvaluationMetrics) Descriptor Uses

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

Deprecated: Use ImageObjectDetectionEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision Uses

func (x *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision() float32

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries Uses

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

func (*ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount Uses

func (x *ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount() int32

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage Uses

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage()

func (*ImageObjectDetectionEvaluationMetrics) ProtoReflect Uses

func (x *ImageObjectDetectionEvaluationMetrics) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionEvaluationMetrics) Reset Uses

func (x *ImageObjectDetectionEvaluationMetrics) Reset()

func (*ImageObjectDetectionEvaluationMetrics) String Uses

func (x *ImageObjectDetectionEvaluationMetrics) String() string

type ImageObjectDetectionModelDeploymentMetadata Uses

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 Uses

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

Deprecated: Use ImageObjectDetectionModelDeploymentMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionModelDeploymentMetadata) GetNodeCount Uses

func (x *ImageObjectDetectionModelDeploymentMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage Uses

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage()

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoReflect Uses

func (x *ImageObjectDetectionModelDeploymentMetadata) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionModelDeploymentMetadata) Reset Uses

func (x *ImageObjectDetectionModelDeploymentMetadata) Reset()

func (*ImageObjectDetectionModelDeploymentMetadata) String Uses

func (x *ImageObjectDetectionModelDeploymentMetadata) String() string

type ImageObjectDetectionModelMetadata Uses

type ImageObjectDetectionModelMetadata struct {

    // Optional. Type of the model. The available values are:
    // *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
    //               calls to AutoML API. Expected to have a higher latency, but
    //               should also have a higher prediction quality than other
    //               models.
    // *   `cloud-low-latency-1` -  A model to be used via prediction
    //               calls to AutoML API. Expected to have low latency, but may
    //               have lower prediction quality than other models.
    // *   `mobile-low-latency-1` - A model that, in addition to providing
    //               prediction via AutoML API, can also be exported (see
    //               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
    //               with TensorFlow afterwards. Expected to have low latency, but
    //               may have lower prediction quality than other models.
    // *   `mobile-versatile-1` - A model that, in addition to providing
    //               prediction via AutoML API, can also be exported (see
    //               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
    //               with TensorFlow afterwards.
    // *   `mobile-high-accuracy-1` - A model that, in addition to providing
    //               prediction via AutoML API, can also be exported (see
    //               [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
    //               with TensorFlow afterwards.  Expected to have a higher
    //               latency, but should also have a higher prediction quality
    //               than other models.
    ModelType string `protobuf:"bytes,1,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`
    // Output only. The number of nodes this model is deployed on. A node is an
    // abstraction of a machine resource, which can handle online prediction QPS
    // as given in the qps_per_node field.
    NodeCount int64 `protobuf:"varint,3,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
    // Output only. An approximate number of online prediction QPS that can
    // be supported by this model per each node on which it is deployed.
    NodeQps float64 `protobuf:"fixed64,4,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`
    // Output only. The reason that this create model operation stopped,
    // e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
    StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`
    // The train budget of creating this model, expressed in milli node
    // hours i.e. 1,000 value in this field means 1 node hour. The actual
    // `train_cost` will be equal or less than this value. If further model
    // training ceases to provide any improvements, it will stop without using
    // full budget and the stop_reason will be `MODEL_CONVERGED`.
    // Note, node_hour  = actual_hour * number_of_nodes_invovled.
    // For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
    // the train budget must be between 20,000 and 900,000 milli node hours,
    // inclusive. The default value is 216, 000 which represents one day in
    // wall time.
    // For model type `mobile-low-latency-1`, `mobile-versatile-1`,
    // `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
    // `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
    // budget must be between 1,000 and 100,000 milli node hours, inclusive.
    // The default value is 24, 000 which represents one day in wall time.
    TrainBudgetMilliNodeHours int64 `protobuf:"varint,6,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"`
    // Output only. The actual train cost of creating this model, expressed in
    // milli node hours, i.e. 1,000 value in this field means 1 node hour.
    // Guaranteed to not exceed the train budget.
    TrainCostMilliNodeHours int64 `protobuf:"varint,7,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,omitempty"`
    // contains filtered or unexported fields
}

Model metadata specific to image object detection.

func (*ImageObjectDetectionModelMetadata) Descriptor Uses

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

Deprecated: Use ImageObjectDetectionModelMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionModelMetadata) GetModelType Uses

func (x *ImageObjectDetectionModelMetadata) GetModelType() string

func (*ImageObjectDetectionModelMetadata) GetNodeCount Uses

func (x *ImageObjectDetectionModelMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelMetadata) GetNodeQps Uses

func (x *ImageObjectDetectionModelMetadata) GetNodeQps() float64

func (*ImageObjectDetectionModelMetadata) GetStopReason Uses

func (x *ImageObjectDetectionModelMetadata) GetStopReason() string

func (*ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours Uses

func (x *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours Uses

func (x *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) ProtoMessage Uses

func (*ImageObjectDetectionModelMetadata) ProtoMessage()

func (*ImageObjectDetectionModelMetadata) ProtoReflect Uses

func (x *ImageObjectDetectionModelMetadata) ProtoReflect() protoreflect.Message

func (*ImageObjectDetectionModelMetadata) Reset Uses

func (x *ImageObjectDetectionModelMetadata) Reset()

func (*ImageObjectDetectionModelMetadata) String Uses

func (x *ImageObjectDetectionModelMetadata) String() string

type Image_ImageBytes Uses

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"`
}

type Image_InputConfig Uses

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"`
}

type ImportDataOperationMetadata Uses

type ImportDataOperationMetadata struct {
    // contains filtered or unexported fields
}

Details of ImportData operation.

func (*ImportDataOperationMetadata) Descriptor Uses

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

Deprecated: Use ImportDataOperationMetadata.ProtoReflect.Descriptor instead.

func (*ImportDataOperationMetadata) ProtoMessage Uses

func (*ImportDataOperationMetadata) ProtoMessage()

func (*ImportDataOperationMetadata) ProtoReflect Uses

func (x *ImportDataOperationMetadata) ProtoReflect() protoreflect.Message

func (*ImportDataOperationMetadata) Reset Uses

func (x *ImportDataOperationMetadata) Reset()

func (*ImportDataOperationMetadata) String Uses

func (x *ImportDataOperationMetadata) String() string

type ImportDataRequest Uses

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 Uses

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

Deprecated: Use ImportDataRequest.ProtoReflect.Descriptor instead.

func (*ImportDataRequest) GetInputConfig Uses

func (x *ImportDataRequest) GetInputConfig() *InputConfig

func (*ImportDataRequest) GetName Uses

func (x *ImportDataRequest) GetName() string

func (*ImportDataRequest) ProtoMessage Uses

func (*ImportDataRequest) ProtoMessage()

func (*ImportDataRequest) ProtoReflect Uses

func (x *ImportDataRequest) ProtoReflect() protoreflect.Message

func (*ImportDataRequest) Reset Uses

func (x *ImportDataRequest) Reset()

func (*ImportDataRequest) String Uses

func (x *ImportDataRequest) String() string

type InputConfig Uses

type InputConfig struct {

    // The source of the input.
    //
    // Types that are assignable to Source:
    //	*InputConfig_GcsSource
    //	*InputConfig_BigquerySource
    Source isInputConfig_Source `protobuf_oneof:"source"`
    // Additional domain-specific parameters describing the semantic of the
    // imported data, any string must be up to 25000
    // characters long.
    //
    // *  For Tables:
    //    `schema_inference_version` - (integer) Required. The version of the
    //        algorithm that should be used for the initial inference of the
    //        schema (columns' DataTypes) of the table the data is being imported
    //        into. Allowed values: "1".
    Params map[string]string `protobuf:"bytes,2,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // contains filtered or unexported fields
}

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.
         TIM