Enum value maps for Model_ModelType.
var (
Model_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "LINEAR_REGRESSION",
2: "LOGISTIC_REGRESSION",
3: "KMEANS",
4: "MATRIX_FACTORIZATION",
5: "DNN_CLASSIFIER",
6: "TENSORFLOW",
7: "DNN_REGRESSOR",
9: "BOOSTED_TREE_REGRESSOR",
10: "BOOSTED_TREE_CLASSIFIER",
11: "ARIMA",
12: "AUTOML_REGRESSOR",
13: "AUTOML_CLASSIFIER",
19: "ARIMA_PLUS",
}
Model_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"LINEAR_REGRESSION": 1,
"LOGISTIC_REGRESSION": 2,
"KMEANS": 3,
"MATRIX_FACTORIZATION": 4,
"DNN_CLASSIFIER": 5,
"TENSORFLOW": 6,
"DNN_REGRESSOR": 7,
"BOOSTED_TREE_REGRESSOR": 9,
"BOOSTED_TREE_CLASSIFIER": 10,
"ARIMA": 11,
"AUTOML_REGRESSOR": 12,
"AUTOML_CLASSIFIER": 13,
"ARIMA_PLUS": 19,
}
)
Enum value maps for Model_LossType.
var (
Model_LossType_name = map[int32]string{
0: "LOSS_TYPE_UNSPECIFIED",
1: "MEAN_SQUARED_LOSS",
2: "MEAN_LOG_LOSS",
}
Model_LossType_value = map[string]int32{
"LOSS_TYPE_UNSPECIFIED": 0,
"MEAN_SQUARED_LOSS": 1,
"MEAN_LOG_LOSS": 2,
}
)
Enum value maps for Model_DistanceType.
var (
Model_DistanceType_name = map[int32]string{
0: "DISTANCE_TYPE_UNSPECIFIED",
1: "EUCLIDEAN",
2: "COSINE",
}
Model_DistanceType_value = map[string]int32{
"DISTANCE_TYPE_UNSPECIFIED": 0,
"EUCLIDEAN": 1,
"COSINE": 2,
}
)
Enum value maps for Model_DataSplitMethod.
var (
Model_DataSplitMethod_name = map[int32]string{
0: "DATA_SPLIT_METHOD_UNSPECIFIED",
1: "RANDOM",
2: "CUSTOM",
3: "SEQUENTIAL",
4: "NO_SPLIT",
5: "AUTO_SPLIT",
}
Model_DataSplitMethod_value = map[string]int32{
"DATA_SPLIT_METHOD_UNSPECIFIED": 0,
"RANDOM": 1,
"CUSTOM": 2,
"SEQUENTIAL": 3,
"NO_SPLIT": 4,
"AUTO_SPLIT": 5,
}
)
Enum value maps for Model_DataFrequency.
var (
Model_DataFrequency_name = map[int32]string{
0: "DATA_FREQUENCY_UNSPECIFIED",
1: "AUTO_FREQUENCY",
2: "YEARLY",
3: "QUARTERLY",
4: "MONTHLY",
5: "WEEKLY",
6: "DAILY",
7: "HOURLY",
8: "PER_MINUTE",
}
Model_DataFrequency_value = map[string]int32{
"DATA_FREQUENCY_UNSPECIFIED": 0,
"AUTO_FREQUENCY": 1,
"YEARLY": 2,
"QUARTERLY": 3,
"MONTHLY": 4,
"WEEKLY": 5,
"DAILY": 6,
"HOURLY": 7,
"PER_MINUTE": 8,
}
)
Enum value maps for Model_HolidayRegion.
var (
Model_HolidayRegion_name = map[int32]string{
0: "HOLIDAY_REGION_UNSPECIFIED",
1: "GLOBAL",
2: "NA",
3: "JAPAC",
4: "EMEA",
5: "LAC",
6: "AE",
7: "AR",
8: "AT",
9: "AU",
10: "BE",
11: "BR",
12: "CA",
13: "CH",
14: "CL",
15: "CN",
16: "CO",
17: "CS",
18: "CZ",
19: "DE",
20: "DK",
21: "DZ",
22: "EC",
23: "EE",
24: "EG",
25: "ES",
26: "FI",
27: "FR",
28: "GB",
29: "GR",
30: "HK",
31: "HU",
32: "ID",
33: "IE",
34: "IL",
35: "IN",
36: "IR",
37: "IT",
38: "JP",
39: "KR",
40: "LV",
41: "MA",
42: "MX",
43: "MY",
44: "NG",
45: "NL",
46: "NO",
47: "NZ",
48: "PE",
49: "PH",
50: "PK",
51: "PL",
52: "PT",
53: "RO",
54: "RS",
55: "RU",
56: "SA",
57: "SE",
58: "SG",
59: "SI",
60: "SK",
61: "TH",
62: "TR",
63: "TW",
64: "UA",
65: "US",
66: "VE",
67: "VN",
68: "ZA",
}
Model_HolidayRegion_value = map[string]int32{
"HOLIDAY_REGION_UNSPECIFIED": 0,
"GLOBAL": 1,
"NA": 2,
"JAPAC": 3,
"EMEA": 4,
"LAC": 5,
"AE": 6,
"AR": 7,
"AT": 8,
"AU": 9,
"BE": 10,
"BR": 11,
"CA": 12,
"CH": 13,
"CL": 14,
"CN": 15,
"CO": 16,
"CS": 17,
"CZ": 18,
"DE": 19,
"DK": 20,
"DZ": 21,
"EC": 22,
"EE": 23,
"EG": 24,
"ES": 25,
"FI": 26,
"FR": 27,
"GB": 28,
"GR": 29,
"HK": 30,
"HU": 31,
"ID": 32,
"IE": 33,
"IL": 34,
"IN": 35,
"IR": 36,
"IT": 37,
"JP": 38,
"KR": 39,
"LV": 40,
"MA": 41,
"MX": 42,
"MY": 43,
"NG": 44,
"NL": 45,
"NO": 46,
"NZ": 47,
"PE": 48,
"PH": 49,
"PK": 50,
"PL": 51,
"PT": 52,
"RO": 53,
"RS": 54,
"RU": 55,
"SA": 56,
"SE": 57,
"SG": 58,
"SI": 59,
"SK": 60,
"TH": 61,
"TR": 62,
"TW": 63,
"UA": 64,
"US": 65,
"VE": 66,
"VN": 67,
"ZA": 68,
}
)
Enum value maps for Model_LearnRateStrategy.
var (
Model_LearnRateStrategy_name = map[int32]string{
0: "LEARN_RATE_STRATEGY_UNSPECIFIED",
1: "LINE_SEARCH",
2: "CONSTANT",
}
Model_LearnRateStrategy_value = map[string]int32{
"LEARN_RATE_STRATEGY_UNSPECIFIED": 0,
"LINE_SEARCH": 1,
"CONSTANT": 2,
}
)
Enum value maps for Model_OptimizationStrategy.
var (
Model_OptimizationStrategy_name = map[int32]string{
0: "OPTIMIZATION_STRATEGY_UNSPECIFIED",
1: "BATCH_GRADIENT_DESCENT",
2: "NORMAL_EQUATION",
}
Model_OptimizationStrategy_value = map[string]int32{
"OPTIMIZATION_STRATEGY_UNSPECIFIED": 0,
"BATCH_GRADIENT_DESCENT": 1,
"NORMAL_EQUATION": 2,
}
)
Enum value maps for Model_FeedbackType.
var (
Model_FeedbackType_name = map[int32]string{
0: "FEEDBACK_TYPE_UNSPECIFIED",
1: "IMPLICIT",
2: "EXPLICIT",
}
Model_FeedbackType_value = map[string]int32{
"FEEDBACK_TYPE_UNSPECIFIED": 0,
"IMPLICIT": 1,
"EXPLICIT": 2,
}
)
Enum value maps for Model_SeasonalPeriod_SeasonalPeriodType.
var (
Model_SeasonalPeriod_SeasonalPeriodType_name = map[int32]string{
0: "SEASONAL_PERIOD_TYPE_UNSPECIFIED",
1: "NO_SEASONALITY",
2: "DAILY",
3: "WEEKLY",
4: "MONTHLY",
5: "QUARTERLY",
6: "YEARLY",
}
Model_SeasonalPeriod_SeasonalPeriodType_value = map[string]int32{
"SEASONAL_PERIOD_TYPE_UNSPECIFIED": 0,
"NO_SEASONALITY": 1,
"DAILY": 2,
"WEEKLY": 3,
"MONTHLY": 4,
"QUARTERLY": 5,
"YEARLY": 6,
}
)
Enum value maps for Model_KmeansEnums_KmeansInitializationMethod.
var (
Model_KmeansEnums_KmeansInitializationMethod_name = map[int32]string{
0: "KMEANS_INITIALIZATION_METHOD_UNSPECIFIED",
1: "RANDOM",
2: "CUSTOM",
3: "KMEANS_PLUS_PLUS",
}
Model_KmeansEnums_KmeansInitializationMethod_value = map[string]int32{
"KMEANS_INITIALIZATION_METHOD_UNSPECIFIED": 0,
"RANDOM": 1,
"CUSTOM": 2,
"KMEANS_PLUS_PLUS": 3,
}
)
Enum value maps for StandardSqlDataType_TypeKind.
var (
StandardSqlDataType_TypeKind_name = map[int32]string{
0: "TYPE_KIND_UNSPECIFIED",
2: "INT64",
5: "BOOL",
7: "FLOAT64",
8: "STRING",
9: "BYTES",
19: "TIMESTAMP",
10: "DATE",
20: "TIME",
21: "DATETIME",
26: "INTERVAL",
22: "GEOGRAPHY",
23: "NUMERIC",
24: "BIGNUMERIC",
25: "JSON",
16: "ARRAY",
17: "STRUCT",
}
StandardSqlDataType_TypeKind_value = map[string]int32{
"TYPE_KIND_UNSPECIFIED": 0,
"INT64": 2,
"BOOL": 5,
"FLOAT64": 7,
"STRING": 8,
"BYTES": 9,
"TIMESTAMP": 19,
"DATE": 10,
"TIME": 20,
"DATETIME": 21,
"INTERVAL": 26,
"GEOGRAPHY": 22,
"NUMERIC": 23,
"BIGNUMERIC": 24,
"JSON": 25,
"ARRAY": 16,
"STRUCT": 17,
}
)
var File_google_cloud_bigquery_v2_encryption_config_proto protoreflect.FileDescriptor
var File_google_cloud_bigquery_v2_model_proto protoreflect.FileDescriptor
var File_google_cloud_bigquery_v2_model_reference_proto protoreflect.FileDescriptor
var File_google_cloud_bigquery_v2_standard_sql_proto protoreflect.FileDescriptor
var File_google_cloud_bigquery_v2_table_reference_proto protoreflect.FileDescriptor
func RegisterModelServiceServer(s *grpc.Server, srv ModelServiceServer)
type DeleteModelRequest struct {
// Required. Project ID of the model to delete.
ProjectId string `protobuf:"bytes,1,opt,name=project_id,json=projectId,proto3" json:"project_id,omitempty"`
// Required. Dataset ID of the model to delete.
DatasetId string `protobuf:"bytes,2,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
// Required. Model ID of the model to delete.
ModelId string `protobuf:"bytes,3,opt,name=model_id,json=modelId,proto3" json:"model_id,omitempty"`
// contains filtered or unexported fields
}
func (*DeleteModelRequest) Descriptor() ([]byte, []int)
Deprecated: Use DeleteModelRequest.ProtoReflect.Descriptor instead.
func (x *DeleteModelRequest) GetDatasetId() string
func (x *DeleteModelRequest) GetModelId() string
func (x *DeleteModelRequest) GetProjectId() string
func (*DeleteModelRequest) ProtoMessage()
func (x *DeleteModelRequest) ProtoReflect() protoreflect.Message
func (x *DeleteModelRequest) Reset()
func (x *DeleteModelRequest) String() string
type EncryptionConfiguration struct {
// Optional. Describes the Cloud KMS encryption key that will be used to
// protect destination BigQuery table. The BigQuery Service Account associated
// with your project requires access to this encryption key.
KmsKeyName *wrapperspb.StringValue `protobuf:"bytes,1,opt,name=kms_key_name,json=kmsKeyName,proto3" json:"kms_key_name,omitempty"`
// contains filtered or unexported fields
}
func (*EncryptionConfiguration) Descriptor() ([]byte, []int)
Deprecated: Use EncryptionConfiguration.ProtoReflect.Descriptor instead.
func (x *EncryptionConfiguration) GetKmsKeyName() *wrapperspb.StringValue
func (*EncryptionConfiguration) ProtoMessage()
func (x *EncryptionConfiguration) ProtoReflect() protoreflect.Message
func (x *EncryptionConfiguration) Reset()
func (x *EncryptionConfiguration) String() string
type GetModelRequest struct {
// Required. Project ID of the requested model.
ProjectId string `protobuf:"bytes,1,opt,name=project_id,json=projectId,proto3" json:"project_id,omitempty"`
// Required. Dataset ID of the requested model.
DatasetId string `protobuf:"bytes,2,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
// Required. Model ID of the requested model.
ModelId string `protobuf:"bytes,3,opt,name=model_id,json=modelId,proto3" json:"model_id,omitempty"`
// contains filtered or unexported fields
}
func (*GetModelRequest) Descriptor() ([]byte, []int)
Deprecated: Use GetModelRequest.ProtoReflect.Descriptor instead.
func (x *GetModelRequest) GetDatasetId() string
func (x *GetModelRequest) GetModelId() string
func (x *GetModelRequest) GetProjectId() string
func (*GetModelRequest) ProtoMessage()
func (x *GetModelRequest) ProtoReflect() protoreflect.Message
func (x *GetModelRequest) Reset()
func (x *GetModelRequest) String() string
type ListModelsRequest struct {
// Required. Project ID of the models to list.
ProjectId string `protobuf:"bytes,1,opt,name=project_id,json=projectId,proto3" json:"project_id,omitempty"`
// Required. Dataset ID of the models to list.
DatasetId string `protobuf:"bytes,2,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
// The maximum number of results to return in a single response page.
// Leverage the page tokens to iterate through the entire collection.
MaxResults *wrapperspb.UInt32Value `protobuf:"bytes,3,opt,name=max_results,json=maxResults,proto3" json:"max_results,omitempty"`
// Page token, returned by a previous call to request the next page of
// results
PageToken string `protobuf:"bytes,4,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
// contains filtered or unexported fields
}
func (*ListModelsRequest) Descriptor() ([]byte, []int)
Deprecated: Use ListModelsRequest.ProtoReflect.Descriptor instead.
func (x *ListModelsRequest) GetDatasetId() string
func (x *ListModelsRequest) GetMaxResults() *wrapperspb.UInt32Value
func (x *ListModelsRequest) GetPageToken() string
func (x *ListModelsRequest) GetProjectId() string
func (*ListModelsRequest) ProtoMessage()
func (x *ListModelsRequest) ProtoReflect() protoreflect.Message
func (x *ListModelsRequest) Reset()
func (x *ListModelsRequest) String() string
type ListModelsResponse struct {
// Models in the requested dataset. Only the following fields are populated:
// model_reference, model_type, creation_time, last_modified_time and
// labels.
Models []*Model `protobuf:"bytes,1,rep,name=models,proto3" json:"models,omitempty"`
// A token to request the next page of results.
NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
// contains filtered or unexported fields
}
func (*ListModelsResponse) Descriptor() ([]byte, []int)
Deprecated: Use ListModelsResponse.ProtoReflect.Descriptor instead.
func (x *ListModelsResponse) GetModels() []*Model
func (x *ListModelsResponse) GetNextPageToken() string
func (*ListModelsResponse) ProtoMessage()
func (x *ListModelsResponse) ProtoReflect() protoreflect.Message
func (x *ListModelsResponse) Reset()
func (x *ListModelsResponse) String() string
type Model struct {
// Output only. A hash of this resource.
Etag string `protobuf:"bytes,1,opt,name=etag,proto3" json:"etag,omitempty"`
// Required. Unique identifier for this model.
ModelReference *ModelReference `protobuf:"bytes,2,opt,name=model_reference,json=modelReference,proto3" json:"model_reference,omitempty"`
// Output only. The time when this model was created, in millisecs since the epoch.
CreationTime int64 `protobuf:"varint,5,opt,name=creation_time,json=creationTime,proto3" json:"creation_time,omitempty"`
// Output only. The time when this model was last modified, in millisecs since the epoch.
LastModifiedTime int64 `protobuf:"varint,6,opt,name=last_modified_time,json=lastModifiedTime,proto3" json:"last_modified_time,omitempty"`
// Optional. A user-friendly description of this model.
Description string `protobuf:"bytes,12,opt,name=description,proto3" json:"description,omitempty"`
// Optional. A descriptive name for this model.
FriendlyName string `protobuf:"bytes,14,opt,name=friendly_name,json=friendlyName,proto3" json:"friendly_name,omitempty"`
// The labels associated with this model. You can use these to organize
// and group your models. Label keys and values can be no longer
// than 63 characters, can only contain lowercase letters, numeric
// characters, underscores and dashes. International characters are allowed.
// Label values are optional. Label keys must start with a letter and each
// label in the list must have a different key.
Labels map[string]string `protobuf:"bytes,15,rep,name=labels,proto3" json:"labels,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Optional. The time when this model expires, in milliseconds since the epoch.
// If not present, the model will persist indefinitely. Expired models
// will be deleted and their storage reclaimed. The defaultTableExpirationMs
// property of the encapsulating dataset can be used to set a default
// expirationTime on newly created models.
ExpirationTime int64 `protobuf:"varint,16,opt,name=expiration_time,json=expirationTime,proto3" json:"expiration_time,omitempty"`
// Output only. The geographic location where the model resides. This value
// is inherited from the dataset.
Location string `protobuf:"bytes,13,opt,name=location,proto3" json:"location,omitempty"`
// Custom encryption configuration (e.g., Cloud KMS keys). This shows the
// encryption configuration of the model data while stored in BigQuery
// storage. This field can be used with PatchModel to update encryption key
// for an already encrypted model.
EncryptionConfiguration *EncryptionConfiguration `protobuf:"bytes,17,opt,name=encryption_configuration,json=encryptionConfiguration,proto3" json:"encryption_configuration,omitempty"`
// Output only. Type of the model resource.
ModelType Model_ModelType `protobuf:"varint,7,opt,name=model_type,json=modelType,proto3,enum=google.cloud.bigquery.v2.Model_ModelType" json:"model_type,omitempty"`
// Output only. Information for all training runs in increasing order of start_time.
TrainingRuns []*Model_TrainingRun `protobuf:"bytes,9,rep,name=training_runs,json=trainingRuns,proto3" json:"training_runs,omitempty"`
// Output only. Input feature columns that were used to train this model.
FeatureColumns []*StandardSqlField `protobuf:"bytes,10,rep,name=feature_columns,json=featureColumns,proto3" json:"feature_columns,omitempty"`
// Output only. Label columns that were used to train this model.
// The output of the model will have a "predicted_" prefix to these columns.
LabelColumns []*StandardSqlField `protobuf:"bytes,11,rep,name=label_columns,json=labelColumns,proto3" json:"label_columns,omitempty"`
// The best trial_id across all training runs.
//
// Deprecated: Do not use.
BestTrialId int64 `protobuf:"varint,19,opt,name=best_trial_id,json=bestTrialId,proto3" json:"best_trial_id,omitempty"`
// contains filtered or unexported fields
}
func (*Model) Descriptor() ([]byte, []int)
Deprecated: Use Model.ProtoReflect.Descriptor instead.
func (x *Model) GetBestTrialId() int64
Deprecated: Do not use.
func (x *Model) GetCreationTime() int64
func (x *Model) GetDescription() string
func (x *Model) GetEncryptionConfiguration() *EncryptionConfiguration
func (x *Model) GetEtag() string
func (x *Model) GetExpirationTime() int64
func (x *Model) GetFeatureColumns() []*StandardSqlField
func (x *Model) GetFriendlyName() string
func (x *Model) GetLabelColumns() []*StandardSqlField
func (x *Model) GetLabels() map[string]string
func (x *Model) GetLastModifiedTime() int64
func (x *Model) GetLocation() string
func (x *Model) GetModelReference() *ModelReference
func (x *Model) GetModelType() Model_ModelType
func (x *Model) GetTrainingRuns() []*Model_TrainingRun
func (*Model) ProtoMessage()
func (x *Model) ProtoReflect() protoreflect.Message
func (x *Model) Reset()
func (x *Model) String() string
Id path of a model.
type ModelReference struct {
// Required. The ID of the project containing this model.
ProjectId string `protobuf:"bytes,1,opt,name=project_id,json=projectId,proto3" json:"project_id,omitempty"`
// Required. The ID of the dataset containing this model.
DatasetId string `protobuf:"bytes,2,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
// Required. The ID of the model. The ID must contain only
// letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum
// length is 1,024 characters.
ModelId string `protobuf:"bytes,3,opt,name=model_id,json=modelId,proto3" json:"model_id,omitempty"`
// contains filtered or unexported fields
}
func (*ModelReference) Descriptor() ([]byte, []int)
Deprecated: Use ModelReference.ProtoReflect.Descriptor instead.
func (x *ModelReference) GetDatasetId() string
func (x *ModelReference) GetModelId() string
func (x *ModelReference) GetProjectId() string
func (*ModelReference) ProtoMessage()
func (x *ModelReference) ProtoReflect() protoreflect.Message
func (x *ModelReference) Reset()
func (x *ModelReference) String() string
ModelServiceClient is the client API for ModelService service.
For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.
type ModelServiceClient interface {
// Gets the specified model resource by model ID.
GetModel(ctx context.Context, in *GetModelRequest, opts ...grpc.CallOption) (*Model, error)
// Lists all models in the specified dataset. Requires the READER dataset
// role. After retrieving the list of models, you can get information about a
// particular model by calling the models.get method.
ListModels(ctx context.Context, in *ListModelsRequest, opts ...grpc.CallOption) (*ListModelsResponse, error)
// Patch specific fields in the specified model.
PatchModel(ctx context.Context, in *PatchModelRequest, opts ...grpc.CallOption) (*Model, error)
// Deletes the model specified by modelId from the dataset.
DeleteModel(ctx context.Context, in *DeleteModelRequest, opts ...grpc.CallOption) (*emptypb.Empty, error)
}
func NewModelServiceClient(cc grpc.ClientConnInterface) ModelServiceClient
ModelServiceServer is the server API for ModelService service.
type ModelServiceServer interface {
// Gets the specified model resource by model ID.
GetModel(context.Context, *GetModelRequest) (*Model, error)
// Lists all models in the specified dataset. Requires the READER dataset
// role. After retrieving the list of models, you can get information about a
// particular model by calling the models.get method.
ListModels(context.Context, *ListModelsRequest) (*ListModelsResponse, error)
// Patch specific fields in the specified model.
PatchModel(context.Context, *PatchModelRequest) (*Model, error)
// Deletes the model specified by modelId from the dataset.
DeleteModel(context.Context, *DeleteModelRequest) (*emptypb.Empty, error)
}
Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows.
type Model_AggregateClassificationMetrics struct {
// Precision is the fraction of actual positive predictions that had
// positive actual labels. For multiclass this is a macro-averaged
// metric treating each class as a binary classifier.
Precision *wrapperspb.DoubleValue `protobuf:"bytes,1,opt,name=precision,proto3" json:"precision,omitempty"`
// Recall is the fraction of actual positive labels that were given a
// positive prediction. For multiclass this is a macro-averaged metric.
Recall *wrapperspb.DoubleValue `protobuf:"bytes,2,opt,name=recall,proto3" json:"recall,omitempty"`
// Accuracy is the fraction of predictions given the correct label. For
// multiclass this is a micro-averaged metric.
Accuracy *wrapperspb.DoubleValue `protobuf:"bytes,3,opt,name=accuracy,proto3" json:"accuracy,omitempty"`
// Threshold at which the metrics are computed. For binary
// classification models this is the positive class threshold.
// For multi-class classfication models this is the confidence
// threshold.
Threshold *wrapperspb.DoubleValue `protobuf:"bytes,4,opt,name=threshold,proto3" json:"threshold,omitempty"`
// The F1 score is an average of recall and precision. For multiclass
// this is a macro-averaged metric.
F1Score *wrapperspb.DoubleValue `protobuf:"bytes,5,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
// Logarithmic Loss. For multiclass this is a macro-averaged metric.
LogLoss *wrapperspb.DoubleValue `protobuf:"bytes,6,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"`
// Area Under a ROC Curve. For multiclass this is a macro-averaged
// metric.
RocAuc *wrapperspb.DoubleValue `protobuf:"bytes,7,opt,name=roc_auc,json=rocAuc,proto3" json:"roc_auc,omitempty"`
// contains filtered or unexported fields
}
func (*Model_AggregateClassificationMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_AggregateClassificationMetrics.ProtoReflect.Descriptor instead.
func (x *Model_AggregateClassificationMetrics) GetAccuracy() *wrapperspb.DoubleValue
func (x *Model_AggregateClassificationMetrics) GetF1Score() *wrapperspb.DoubleValue
func (x *Model_AggregateClassificationMetrics) GetLogLoss() *wrapperspb.DoubleValue
func (x *Model_AggregateClassificationMetrics) GetPrecision() *wrapperspb.DoubleValue
func (x *Model_AggregateClassificationMetrics) GetRecall() *wrapperspb.DoubleValue
func (x *Model_AggregateClassificationMetrics) GetRocAuc() *wrapperspb.DoubleValue
func (x *Model_AggregateClassificationMetrics) GetThreshold() *wrapperspb.DoubleValue
func (*Model_AggregateClassificationMetrics) ProtoMessage()
func (x *Model_AggregateClassificationMetrics) ProtoReflect() protoreflect.Message
func (x *Model_AggregateClassificationMetrics) Reset()
func (x *Model_AggregateClassificationMetrics) String() string
ARIMA model fitting metrics.
type Model_ArimaFittingMetrics struct {
// Log-likelihood.
LogLikelihood float64 `protobuf:"fixed64,1,opt,name=log_likelihood,json=logLikelihood,proto3" json:"log_likelihood,omitempty"`
// AIC.
Aic float64 `protobuf:"fixed64,2,opt,name=aic,proto3" json:"aic,omitempty"`
// Variance.
Variance float64 `protobuf:"fixed64,3,opt,name=variance,proto3" json:"variance,omitempty"`
// contains filtered or unexported fields
}
func (*Model_ArimaFittingMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_ArimaFittingMetrics.ProtoReflect.Descriptor instead.
func (x *Model_ArimaFittingMetrics) GetAic() float64
func (x *Model_ArimaFittingMetrics) GetLogLikelihood() float64
func (x *Model_ArimaFittingMetrics) GetVariance() float64
func (*Model_ArimaFittingMetrics) ProtoMessage()
func (x *Model_ArimaFittingMetrics) ProtoReflect() protoreflect.Message
func (x *Model_ArimaFittingMetrics) Reset()
func (x *Model_ArimaFittingMetrics) String() string
Model evaluation metrics for ARIMA forecasting models.
type Model_ArimaForecastingMetrics struct {
// Non-seasonal order.
//
// Deprecated: Do not use.
NonSeasonalOrder []*Model_ArimaOrder `protobuf:"bytes,1,rep,name=non_seasonal_order,json=nonSeasonalOrder,proto3" json:"non_seasonal_order,omitempty"`
// Arima model fitting metrics.
//
// Deprecated: Do not use.
ArimaFittingMetrics []*Model_ArimaFittingMetrics `protobuf:"bytes,2,rep,name=arima_fitting_metrics,json=arimaFittingMetrics,proto3" json:"arima_fitting_metrics,omitempty"`
// Seasonal periods. Repeated because multiple periods are supported for one
// time series.
//
// Deprecated: Do not use.
SeasonalPeriods []Model_SeasonalPeriod_SeasonalPeriodType `protobuf:"varint,3,rep,packed,name=seasonal_periods,json=seasonalPeriods,proto3,enum=google.cloud.bigquery.v2.Model_SeasonalPeriod_SeasonalPeriodType" json:"seasonal_periods,omitempty"`
// Whether Arima model fitted with drift or not. It is always false when d
// is not 1.
//
// Deprecated: Do not use.
HasDrift []bool `protobuf:"varint,4,rep,packed,name=has_drift,json=hasDrift,proto3" json:"has_drift,omitempty"`
// Id to differentiate different time series for the large-scale case.
//
// Deprecated: Do not use.
TimeSeriesId []string `protobuf:"bytes,5,rep,name=time_series_id,json=timeSeriesId,proto3" json:"time_series_id,omitempty"`
// Repeated as there can be many metric sets (one for each model) in
// auto-arima and the large-scale case.
ArimaSingleModelForecastingMetrics []*Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics `protobuf:"bytes,6,rep,name=arima_single_model_forecasting_metrics,json=arimaSingleModelForecastingMetrics,proto3" json:"arima_single_model_forecasting_metrics,omitempty"`
// contains filtered or unexported fields
}
func (*Model_ArimaForecastingMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_ArimaForecastingMetrics.ProtoReflect.Descriptor instead.
func (x *Model_ArimaForecastingMetrics) GetArimaFittingMetrics() []*Model_ArimaFittingMetrics
Deprecated: Do not use.
func (x *Model_ArimaForecastingMetrics) GetArimaSingleModelForecastingMetrics() []*Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics
func (x *Model_ArimaForecastingMetrics) GetHasDrift() []bool
Deprecated: Do not use.
func (x *Model_ArimaForecastingMetrics) GetNonSeasonalOrder() []*Model_ArimaOrder
Deprecated: Do not use.
func (x *Model_ArimaForecastingMetrics) GetSeasonalPeriods() []Model_SeasonalPeriod_SeasonalPeriodType
Deprecated: Do not use.
func (x *Model_ArimaForecastingMetrics) GetTimeSeriesId() []string
Deprecated: Do not use.
func (*Model_ArimaForecastingMetrics) ProtoMessage()
func (x *Model_ArimaForecastingMetrics) ProtoReflect() protoreflect.Message
func (x *Model_ArimaForecastingMetrics) Reset()
func (x *Model_ArimaForecastingMetrics) String() string
Model evaluation metrics for a single ARIMA forecasting model.
type Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics struct {
// Non-seasonal order.
NonSeasonalOrder *Model_ArimaOrder `protobuf:"bytes,1,opt,name=non_seasonal_order,json=nonSeasonalOrder,proto3" json:"non_seasonal_order,omitempty"`
// Arima fitting metrics.
ArimaFittingMetrics *Model_ArimaFittingMetrics `protobuf:"bytes,2,opt,name=arima_fitting_metrics,json=arimaFittingMetrics,proto3" json:"arima_fitting_metrics,omitempty"`
// Is arima model fitted with drift or not. It is always false when d
// is not 1.
HasDrift bool `protobuf:"varint,3,opt,name=has_drift,json=hasDrift,proto3" json:"has_drift,omitempty"`
// The time_series_id value for this time series. It will be one of
// the unique values from the time_series_id_column specified during
// ARIMA model training. Only present when time_series_id_column
// training option was used.
TimeSeriesId string `protobuf:"bytes,4,opt,name=time_series_id,json=timeSeriesId,proto3" json:"time_series_id,omitempty"`
// The tuple of time_series_ids identifying this time series. It will
// be one of the unique tuples of values present in the
// time_series_id_columns specified during ARIMA model training. Only
// present when time_series_id_columns training option was used and
// the order of values here are same as the order of
// time_series_id_columns.
TimeSeriesIds []string `protobuf:"bytes,9,rep,name=time_series_ids,json=timeSeriesIds,proto3" json:"time_series_ids,omitempty"`
// Seasonal periods. Repeated because multiple periods are supported
// for one time series.
SeasonalPeriods []Model_SeasonalPeriod_SeasonalPeriodType `protobuf:"varint,5,rep,packed,name=seasonal_periods,json=seasonalPeriods,proto3,enum=google.cloud.bigquery.v2.Model_SeasonalPeriod_SeasonalPeriodType" json:"seasonal_periods,omitempty"`
// If true, holiday_effect is a part of time series decomposition result.
HasHolidayEffect *wrapperspb.BoolValue `protobuf:"bytes,6,opt,name=has_holiday_effect,json=hasHolidayEffect,proto3" json:"has_holiday_effect,omitempty"`
// If true, spikes_and_dips is a part of time series decomposition result.
HasSpikesAndDips *wrapperspb.BoolValue `protobuf:"bytes,7,opt,name=has_spikes_and_dips,json=hasSpikesAndDips,proto3" json:"has_spikes_and_dips,omitempty"`
// If true, step_changes is a part of time series decomposition result.
HasStepChanges *wrapperspb.BoolValue `protobuf:"bytes,8,opt,name=has_step_changes,json=hasStepChanges,proto3" json:"has_step_changes,omitempty"`
// contains filtered or unexported fields
}
func (*Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics.ProtoReflect.Descriptor instead.
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetArimaFittingMetrics() *Model_ArimaFittingMetrics
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetHasDrift() bool
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetHasHolidayEffect() *wrapperspb.BoolValue
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetHasSpikesAndDips() *wrapperspb.BoolValue
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetHasStepChanges() *wrapperspb.BoolValue
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetNonSeasonalOrder() *Model_ArimaOrder
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetSeasonalPeriods() []Model_SeasonalPeriod_SeasonalPeriodType
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetTimeSeriesId() string
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) GetTimeSeriesIds() []string
func (*Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) ProtoMessage()
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) ProtoReflect() protoreflect.Message
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) Reset()
func (x *Model_ArimaForecastingMetrics_ArimaSingleModelForecastingMetrics) String() string
Arima order, can be used for both non-seasonal and seasonal parts.
type Model_ArimaOrder struct {
// Order of the autoregressive part.
P int64 `protobuf:"varint,1,opt,name=p,proto3" json:"p,omitempty"`
// Order of the differencing part.
D int64 `protobuf:"varint,2,opt,name=d,proto3" json:"d,omitempty"`
// Order of the moving-average part.
Q int64 `protobuf:"varint,3,opt,name=q,proto3" json:"q,omitempty"`
// contains filtered or unexported fields
}
func (*Model_ArimaOrder) Descriptor() ([]byte, []int)
Deprecated: Use Model_ArimaOrder.ProtoReflect.Descriptor instead.
func (x *Model_ArimaOrder) GetD() int64
func (x *Model_ArimaOrder) GetP() int64
func (x *Model_ArimaOrder) GetQ() int64
func (*Model_ArimaOrder) ProtoMessage()
func (x *Model_ArimaOrder) ProtoReflect() protoreflect.Message
func (x *Model_ArimaOrder) Reset()
func (x *Model_ArimaOrder) String() string
Evaluation metrics for binary classification/classifier models.
type Model_BinaryClassificationMetrics struct {
// Aggregate classification metrics.
AggregateClassificationMetrics *Model_AggregateClassificationMetrics `protobuf:"bytes,1,opt,name=aggregate_classification_metrics,json=aggregateClassificationMetrics,proto3" json:"aggregate_classification_metrics,omitempty"`
// Binary confusion matrix at multiple thresholds.
BinaryConfusionMatrixList []*Model_BinaryClassificationMetrics_BinaryConfusionMatrix `protobuf:"bytes,2,rep,name=binary_confusion_matrix_list,json=binaryConfusionMatrixList,proto3" json:"binary_confusion_matrix_list,omitempty"`
// Label representing the positive class.
PositiveLabel string `protobuf:"bytes,3,opt,name=positive_label,json=positiveLabel,proto3" json:"positive_label,omitempty"`
// Label representing the negative class.
NegativeLabel string `protobuf:"bytes,4,opt,name=negative_label,json=negativeLabel,proto3" json:"negative_label,omitempty"`
// contains filtered or unexported fields
}
func (*Model_BinaryClassificationMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_BinaryClassificationMetrics.ProtoReflect.Descriptor instead.
func (x *Model_BinaryClassificationMetrics) GetAggregateClassificationMetrics() *Model_AggregateClassificationMetrics
func (x *Model_BinaryClassificationMetrics) GetBinaryConfusionMatrixList() []*Model_BinaryClassificationMetrics_BinaryConfusionMatrix
func (x *Model_BinaryClassificationMetrics) GetNegativeLabel() string
func (x *Model_BinaryClassificationMetrics) GetPositiveLabel() string
func (*Model_BinaryClassificationMetrics) ProtoMessage()
func (x *Model_BinaryClassificationMetrics) ProtoReflect() protoreflect.Message
func (x *Model_BinaryClassificationMetrics) Reset()
func (x *Model_BinaryClassificationMetrics) String() string
Confusion matrix for binary classification models.
type Model_BinaryClassificationMetrics_BinaryConfusionMatrix struct {
// Threshold value used when computing each of the following metric.
PositiveClassThreshold *wrapperspb.DoubleValue `protobuf:"bytes,1,opt,name=positive_class_threshold,json=positiveClassThreshold,proto3" json:"positive_class_threshold,omitempty"`
// Number of true samples predicted as true.
TruePositives *wrapperspb.Int64Value `protobuf:"bytes,2,opt,name=true_positives,json=truePositives,proto3" json:"true_positives,omitempty"`
// Number of false samples predicted as true.
FalsePositives *wrapperspb.Int64Value `protobuf:"bytes,3,opt,name=false_positives,json=falsePositives,proto3" json:"false_positives,omitempty"`
// Number of true samples predicted as false.
TrueNegatives *wrapperspb.Int64Value `protobuf:"bytes,4,opt,name=true_negatives,json=trueNegatives,proto3" json:"true_negatives,omitempty"`
// Number of false samples predicted as false.
FalseNegatives *wrapperspb.Int64Value `protobuf:"bytes,5,opt,name=false_negatives,json=falseNegatives,proto3" json:"false_negatives,omitempty"`
// The fraction of actual positive predictions that had positive actual
// labels.
Precision *wrapperspb.DoubleValue `protobuf:"bytes,6,opt,name=precision,proto3" json:"precision,omitempty"`
// The fraction of actual positive labels that were given a positive
// prediction.
Recall *wrapperspb.DoubleValue `protobuf:"bytes,7,opt,name=recall,proto3" json:"recall,omitempty"`
// The equally weighted average of recall and precision.
F1Score *wrapperspb.DoubleValue `protobuf:"bytes,8,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
// The fraction of predictions given the correct label.
Accuracy *wrapperspb.DoubleValue `protobuf:"bytes,9,opt,name=accuracy,proto3" json:"accuracy,omitempty"`
// contains filtered or unexported fields
}
func (*Model_BinaryClassificationMetrics_BinaryConfusionMatrix) Descriptor() ([]byte, []int)
Deprecated: Use Model_BinaryClassificationMetrics_BinaryConfusionMatrix.ProtoReflect.Descriptor instead.
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetAccuracy() *wrapperspb.DoubleValue
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetF1Score() *wrapperspb.DoubleValue
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetFalseNegatives() *wrapperspb.Int64Value
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetFalsePositives() *wrapperspb.Int64Value
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetPositiveClassThreshold() *wrapperspb.DoubleValue
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetPrecision() *wrapperspb.DoubleValue
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetRecall() *wrapperspb.DoubleValue
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetTrueNegatives() *wrapperspb.Int64Value
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetTruePositives() *wrapperspb.Int64Value
func (*Model_BinaryClassificationMetrics_BinaryConfusionMatrix) ProtoMessage()
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) ProtoReflect() protoreflect.Message
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) Reset()
func (x *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) String() string
Evaluation metrics for clustering models.
type Model_ClusteringMetrics struct {
// Davies-Bouldin index.
DaviesBouldinIndex *wrapperspb.DoubleValue `protobuf:"bytes,1,opt,name=davies_bouldin_index,json=daviesBouldinIndex,proto3" json:"davies_bouldin_index,omitempty"`
// Mean of squared distances between each sample to its cluster centroid.
MeanSquaredDistance *wrapperspb.DoubleValue `protobuf:"bytes,2,opt,name=mean_squared_distance,json=meanSquaredDistance,proto3" json:"mean_squared_distance,omitempty"`
// Information for all clusters.
Clusters []*Model_ClusteringMetrics_Cluster `protobuf:"bytes,3,rep,name=clusters,proto3" json:"clusters,omitempty"`
// contains filtered or unexported fields
}
func (*Model_ClusteringMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_ClusteringMetrics.ProtoReflect.Descriptor instead.
func (x *Model_ClusteringMetrics) GetClusters() []*Model_ClusteringMetrics_Cluster
func (x *Model_ClusteringMetrics) GetDaviesBouldinIndex() *wrapperspb.DoubleValue
func (x *Model_ClusteringMetrics) GetMeanSquaredDistance() *wrapperspb.DoubleValue
func (*Model_ClusteringMetrics) ProtoMessage()
func (x *Model_ClusteringMetrics) ProtoReflect() protoreflect.Message
func (x *Model_ClusteringMetrics) Reset()
func (x *Model_ClusteringMetrics) String() string
Message containing the information about one cluster.
type Model_ClusteringMetrics_Cluster struct {
// Centroid id.
CentroidId int64 `protobuf:"varint,1,opt,name=centroid_id,json=centroidId,proto3" json:"centroid_id,omitempty"`
// Values of highly variant features for this cluster.
FeatureValues []*Model_ClusteringMetrics_Cluster_FeatureValue `protobuf:"bytes,2,rep,name=feature_values,json=featureValues,proto3" json:"feature_values,omitempty"`
// Count of training data rows that were assigned to this cluster.
Count *wrapperspb.Int64Value `protobuf:"bytes,3,opt,name=count,proto3" json:"count,omitempty"`
// contains filtered or unexported fields
}
func (*Model_ClusteringMetrics_Cluster) Descriptor() ([]byte, []int)
Deprecated: Use Model_ClusteringMetrics_Cluster.ProtoReflect.Descriptor instead.
func (x *Model_ClusteringMetrics_Cluster) GetCentroidId() int64
func (x *Model_ClusteringMetrics_Cluster) GetCount() *wrapperspb.Int64Value
func (x *Model_ClusteringMetrics_Cluster) GetFeatureValues() []*Model_ClusteringMetrics_Cluster_FeatureValue
func (*Model_ClusteringMetrics_Cluster) ProtoMessage()
func (x *Model_ClusteringMetrics_Cluster) ProtoReflect() protoreflect.Message
func (x *Model_ClusteringMetrics_Cluster) Reset()
func (x *Model_ClusteringMetrics_Cluster) String() string
Representative value of a single feature within the cluster.
type Model_ClusteringMetrics_Cluster_FeatureValue struct {
// The feature column name.
FeatureColumn string `protobuf:"bytes,1,opt,name=feature_column,json=featureColumn,proto3" json:"feature_column,omitempty"`
// Types that are assignable to Value:
// *Model_ClusteringMetrics_Cluster_FeatureValue_NumericalValue
// *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_
Value isModel_ClusteringMetrics_Cluster_FeatureValue_Value `protobuf_oneof:"value"`
// contains filtered or unexported fields
}
func (*Model_ClusteringMetrics_Cluster_FeatureValue) Descriptor() ([]byte, []int)
Deprecated: Use Model_ClusteringMetrics_Cluster_FeatureValue.ProtoReflect.Descriptor instead.
func (x *Model_ClusteringMetrics_Cluster_FeatureValue) GetCategoricalValue() *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue
func (x *Model_ClusteringMetrics_Cluster_FeatureValue) GetFeatureColumn() string
func (x *Model_ClusteringMetrics_Cluster_FeatureValue) GetNumericalValue() *wrapperspb.DoubleValue
func (m *Model_ClusteringMetrics_Cluster_FeatureValue) GetValue() isModel_ClusteringMetrics_Cluster_FeatureValue_Value
func (*Model_ClusteringMetrics_Cluster_FeatureValue) ProtoMessage()
func (x *Model_ClusteringMetrics_Cluster_FeatureValue) ProtoReflect() protoreflect.Message
func (x *Model_ClusteringMetrics_Cluster_FeatureValue) Reset()
func (x *Model_ClusteringMetrics_Cluster_FeatureValue) String() string
Representative value of a categorical feature.
type Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue struct {
// Counts of all categories for the categorical feature. If there are
// more than ten categories, we return top ten (by count) and return
// one more CategoryCount with category "_OTHER_" and count as
// aggregate counts of remaining categories.
CategoryCounts []*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount `protobuf:"bytes,1,rep,name=category_counts,json=categoryCounts,proto3" json:"category_counts,omitempty"`
// contains filtered or unexported fields
}
func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) Descriptor() ([]byte, []int)
Deprecated: Use Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue.ProtoReflect.Descriptor instead.
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) GetCategoryCounts() []*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount
func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) ProtoMessage()
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) ProtoReflect() protoreflect.Message
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) Reset()
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) String() string
type Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_ struct {
// The categorical feature value.
CategoricalValue *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue `protobuf:"bytes,3,opt,name=categorical_value,json=categoricalValue,proto3,oneof"`
}
Represents the count of a single category within the cluster.
type Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount struct {
// The name of category.
Category string `protobuf:"bytes,1,opt,name=category,proto3" json:"category,omitempty"`
// The count of training samples matching the category within the
// cluster.
Count *wrapperspb.Int64Value `protobuf:"bytes,2,opt,name=count,proto3" json:"count,omitempty"`
// contains filtered or unexported fields
}
func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) Descriptor() ([]byte, []int)
Deprecated: Use Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount.ProtoReflect.Descriptor instead.
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) GetCategory() string
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) GetCount() *wrapperspb.Int64Value
func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) ProtoMessage()
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) ProtoReflect() protoreflect.Message
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) Reset()
func (x *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) String() string
type Model_ClusteringMetrics_Cluster_FeatureValue_NumericalValue struct {
// The numerical feature value. This is the centroid value for this
// feature.
NumericalValue *wrapperspb.DoubleValue `protobuf:"bytes,2,opt,name=numerical_value,json=numericalValue,proto3,oneof"`
}
Type of supported data frequency for time series forecasting models.
type Model_DataFrequency int32
const (
Model_DATA_FREQUENCY_UNSPECIFIED Model_DataFrequency = 0
// Automatically inferred from timestamps.
Model_AUTO_FREQUENCY Model_DataFrequency = 1
// Yearly data.
Model_YEARLY Model_DataFrequency = 2
// Quarterly data.
Model_QUARTERLY Model_DataFrequency = 3
// Monthly data.
Model_MONTHLY Model_DataFrequency = 4
// Weekly data.
Model_WEEKLY Model_DataFrequency = 5
// Daily data.
Model_DAILY Model_DataFrequency = 6
// Hourly data.
Model_HOURLY Model_DataFrequency = 7
// Per-minute data.
Model_PER_MINUTE Model_DataFrequency = 8
)
func (Model_DataFrequency) Descriptor() protoreflect.EnumDescriptor
func (x Model_DataFrequency) Enum() *Model_DataFrequency
func (Model_DataFrequency) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_DataFrequency.Descriptor instead.
func (x Model_DataFrequency) Number() protoreflect.EnumNumber
func (x Model_DataFrequency) String() string
func (Model_DataFrequency) Type() protoreflect.EnumType
Indicates the method to split input data into multiple tables.
type Model_DataSplitMethod int32
const (
Model_DATA_SPLIT_METHOD_UNSPECIFIED Model_DataSplitMethod = 0
// Splits data randomly.
Model_RANDOM Model_DataSplitMethod = 1
// Splits data with the user provided tags.
Model_CUSTOM Model_DataSplitMethod = 2
// Splits data sequentially.
Model_SEQUENTIAL Model_DataSplitMethod = 3
// Data split will be skipped.
Model_NO_SPLIT Model_DataSplitMethod = 4
// Splits data automatically: Uses NO_SPLIT if the data size is small.
// Otherwise uses RANDOM.
Model_AUTO_SPLIT Model_DataSplitMethod = 5
)
func (Model_DataSplitMethod) Descriptor() protoreflect.EnumDescriptor
func (x Model_DataSplitMethod) Enum() *Model_DataSplitMethod
func (Model_DataSplitMethod) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_DataSplitMethod.Descriptor instead.
func (x Model_DataSplitMethod) Number() protoreflect.EnumNumber
func (x Model_DataSplitMethod) String() string
func (Model_DataSplitMethod) Type() protoreflect.EnumType
Data split result. This contains references to the training and evaluation data tables that were used to train the model.
type Model_DataSplitResult struct {
// Table reference of the training data after split.
TrainingTable *TableReference `protobuf:"bytes,1,opt,name=training_table,json=trainingTable,proto3" json:"training_table,omitempty"`
// Table reference of the evaluation data after split.
EvaluationTable *TableReference `protobuf:"bytes,2,opt,name=evaluation_table,json=evaluationTable,proto3" json:"evaluation_table,omitempty"`
// contains filtered or unexported fields
}
func (*Model_DataSplitResult) Descriptor() ([]byte, []int)
Deprecated: Use Model_DataSplitResult.ProtoReflect.Descriptor instead.
func (x *Model_DataSplitResult) GetEvaluationTable() *TableReference
func (x *Model_DataSplitResult) GetTrainingTable() *TableReference
func (*Model_DataSplitResult) ProtoMessage()
func (x *Model_DataSplitResult) ProtoReflect() protoreflect.Message
func (x *Model_DataSplitResult) Reset()
func (x *Model_DataSplitResult) String() string
Distance metric used to compute the distance between two points.
type Model_DistanceType int32
const (
Model_DISTANCE_TYPE_UNSPECIFIED Model_DistanceType = 0
// Eculidean distance.
Model_EUCLIDEAN Model_DistanceType = 1
// Cosine distance.
Model_COSINE Model_DistanceType = 2
)
func (Model_DistanceType) Descriptor() protoreflect.EnumDescriptor
func (x Model_DistanceType) Enum() *Model_DistanceType
func (Model_DistanceType) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_DistanceType.Descriptor instead.
func (x Model_DistanceType) Number() protoreflect.EnumNumber
func (x Model_DistanceType) String() string
func (Model_DistanceType) Type() protoreflect.EnumType
Evaluation metrics of a model. These are either computed on all training data or just the eval data based on whether eval data was used during training. These are not present for imported models.
type Model_EvaluationMetrics struct {
// Types that are assignable to Metrics:
// *Model_EvaluationMetrics_RegressionMetrics
// *Model_EvaluationMetrics_BinaryClassificationMetrics
// *Model_EvaluationMetrics_MultiClassClassificationMetrics
// *Model_EvaluationMetrics_ClusteringMetrics
// *Model_EvaluationMetrics_RankingMetrics
// *Model_EvaluationMetrics_ArimaForecastingMetrics
Metrics isModel_EvaluationMetrics_Metrics `protobuf_oneof:"metrics"`
// contains filtered or unexported fields
}
func (*Model_EvaluationMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_EvaluationMetrics.ProtoReflect.Descriptor instead.
func (x *Model_EvaluationMetrics) GetArimaForecastingMetrics() *Model_ArimaForecastingMetrics
func (x *Model_EvaluationMetrics) GetBinaryClassificationMetrics() *Model_BinaryClassificationMetrics
func (x *Model_EvaluationMetrics) GetClusteringMetrics() *Model_ClusteringMetrics
func (m *Model_EvaluationMetrics) GetMetrics() isModel_EvaluationMetrics_Metrics
func (x *Model_EvaluationMetrics) GetMultiClassClassificationMetrics() *Model_MultiClassClassificationMetrics
func (x *Model_EvaluationMetrics) GetRankingMetrics() *Model_RankingMetrics
func (x *Model_EvaluationMetrics) GetRegressionMetrics() *Model_RegressionMetrics
func (*Model_EvaluationMetrics) ProtoMessage()
func (x *Model_EvaluationMetrics) ProtoReflect() protoreflect.Message
func (x *Model_EvaluationMetrics) Reset()
func (x *Model_EvaluationMetrics) String() string
type Model_EvaluationMetrics_ArimaForecastingMetrics struct {
// Populated for ARIMA models.
ArimaForecastingMetrics *Model_ArimaForecastingMetrics `protobuf:"bytes,6,opt,name=arima_forecasting_metrics,json=arimaForecastingMetrics,proto3,oneof"`
}
type Model_EvaluationMetrics_BinaryClassificationMetrics struct {
// Populated for binary classification/classifier models.
BinaryClassificationMetrics *Model_BinaryClassificationMetrics `protobuf:"bytes,2,opt,name=binary_classification_metrics,json=binaryClassificationMetrics,proto3,oneof"`
}
type Model_EvaluationMetrics_ClusteringMetrics struct {
// Populated for clustering models.
ClusteringMetrics *Model_ClusteringMetrics `protobuf:"bytes,4,opt,name=clustering_metrics,json=clusteringMetrics,proto3,oneof"`
}
type Model_EvaluationMetrics_MultiClassClassificationMetrics struct {
// Populated for multi-class classification/classifier models.
MultiClassClassificationMetrics *Model_MultiClassClassificationMetrics `protobuf:"bytes,3,opt,name=multi_class_classification_metrics,json=multiClassClassificationMetrics,proto3,oneof"`
}
type Model_EvaluationMetrics_RankingMetrics struct {
// Populated for implicit feedback type matrix factorization models.
RankingMetrics *Model_RankingMetrics `protobuf:"bytes,5,opt,name=ranking_metrics,json=rankingMetrics,proto3,oneof"`
}
type Model_EvaluationMetrics_RegressionMetrics struct {
// Populated for regression models and explicit feedback type matrix
// factorization models.
RegressionMetrics *Model_RegressionMetrics `protobuf:"bytes,1,opt,name=regression_metrics,json=regressionMetrics,proto3,oneof"`
}
Indicates the training algorithm to use for matrix factorization models.
type Model_FeedbackType int32
const (
Model_FEEDBACK_TYPE_UNSPECIFIED Model_FeedbackType = 0
// Use weighted-als for implicit feedback problems.
Model_IMPLICIT Model_FeedbackType = 1
// Use nonweighted-als for explicit feedback problems.
Model_EXPLICIT Model_FeedbackType = 2
)
func (Model_FeedbackType) Descriptor() protoreflect.EnumDescriptor
func (x Model_FeedbackType) Enum() *Model_FeedbackType
func (Model_FeedbackType) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_FeedbackType.Descriptor instead.
func (x Model_FeedbackType) Number() protoreflect.EnumNumber
func (x Model_FeedbackType) String() string
func (Model_FeedbackType) Type() protoreflect.EnumType
Global explanations containing the top most important features after training.
type Model_GlobalExplanation struct {
// A list of the top global explanations. Sorted by absolute value of
// attribution in descending order.
Explanations []*Model_GlobalExplanation_Explanation `protobuf:"bytes,1,rep,name=explanations,proto3" json:"explanations,omitempty"`
// Class label for this set of global explanations. Will be empty/null for
// binary logistic and linear regression models. Sorted alphabetically in
// descending order.
ClassLabel string `protobuf:"bytes,2,opt,name=class_label,json=classLabel,proto3" json:"class_label,omitempty"`
// contains filtered or unexported fields
}
func (*Model_GlobalExplanation) Descriptor() ([]byte, []int)
Deprecated: Use Model_GlobalExplanation.ProtoReflect.Descriptor instead.
func (x *Model_GlobalExplanation) GetClassLabel() string
func (x *Model_GlobalExplanation) GetExplanations() []*Model_GlobalExplanation_Explanation
func (*Model_GlobalExplanation) ProtoMessage()
func (x *Model_GlobalExplanation) ProtoReflect() protoreflect.Message
func (x *Model_GlobalExplanation) Reset()
func (x *Model_GlobalExplanation) String() string
Explanation for a single feature.
type Model_GlobalExplanation_Explanation struct {
// Full name of the feature. For non-numerical features, will be
// formatted like <column_name>.<encoded_feature_name>. Overall size of
// feature name will always be truncated to first 120 characters.
FeatureName string `protobuf:"bytes,1,opt,name=feature_name,json=featureName,proto3" json:"feature_name,omitempty"`
// Attribution of feature.
Attribution *wrapperspb.DoubleValue `protobuf:"bytes,2,opt,name=attribution,proto3" json:"attribution,omitempty"`
// contains filtered or unexported fields
}
func (*Model_GlobalExplanation_Explanation) Descriptor() ([]byte, []int)
Deprecated: Use Model_GlobalExplanation_Explanation.ProtoReflect.Descriptor instead.
func (x *Model_GlobalExplanation_Explanation) GetAttribution() *wrapperspb.DoubleValue
func (x *Model_GlobalExplanation_Explanation) GetFeatureName() string
func (*Model_GlobalExplanation_Explanation) ProtoMessage()
func (x *Model_GlobalExplanation_Explanation) ProtoReflect() protoreflect.Message
func (x *Model_GlobalExplanation_Explanation) Reset()
func (x *Model_GlobalExplanation_Explanation) String() string
Type of supported holiday regions for time series forecasting models.
type Model_HolidayRegion int32
const (
// Holiday region unspecified.
Model_HOLIDAY_REGION_UNSPECIFIED Model_HolidayRegion = 0
// Global.
Model_GLOBAL Model_HolidayRegion = 1
// North America.
Model_NA Model_HolidayRegion = 2
// Japan and Asia Pacific: Korea, Greater China, India, Australia, and New
// Zealand.
Model_JAPAC Model_HolidayRegion = 3
// Europe, the Middle East and Africa.
Model_EMEA Model_HolidayRegion = 4
// Latin America and the Caribbean.
Model_LAC Model_HolidayRegion = 5
// United Arab Emirates
Model_AE Model_HolidayRegion = 6
// Argentina
Model_AR Model_HolidayRegion = 7
// Austria
Model_AT Model_HolidayRegion = 8
// Australia
Model_AU Model_HolidayRegion = 9
// Belgium
Model_BE Model_HolidayRegion = 10
// Brazil
Model_BR Model_HolidayRegion = 11
// Canada
Model_CA Model_HolidayRegion = 12
// Switzerland
Model_CH Model_HolidayRegion = 13
// Chile
Model_CL Model_HolidayRegion = 14
// China
Model_CN Model_HolidayRegion = 15
// Colombia
Model_CO Model_HolidayRegion = 16
// Czechoslovakia
Model_CS Model_HolidayRegion = 17
// Czech Republic
Model_CZ Model_HolidayRegion = 18
// Germany
Model_DE Model_HolidayRegion = 19
// Denmark
Model_DK Model_HolidayRegion = 20
// Algeria
Model_DZ Model_HolidayRegion = 21
// Ecuador
Model_EC Model_HolidayRegion = 22
// Estonia
Model_EE Model_HolidayRegion = 23
// Egypt
Model_EG Model_HolidayRegion = 24
// Spain
Model_ES Model_HolidayRegion = 25
// Finland
Model_FI Model_HolidayRegion = 26
// France
Model_FR Model_HolidayRegion = 27
// Great Britain (United Kingdom)
Model_GB Model_HolidayRegion = 28
// Greece
Model_GR Model_HolidayRegion = 29
// Hong Kong
Model_HK Model_HolidayRegion = 30
// Hungary
Model_HU Model_HolidayRegion = 31
// Indonesia
Model_ID Model_HolidayRegion = 32
// Ireland
Model_IE Model_HolidayRegion = 33
// Israel
Model_IL Model_HolidayRegion = 34
// India
Model_IN Model_HolidayRegion = 35
// Iran
Model_IR Model_HolidayRegion = 36
// Italy
Model_IT Model_HolidayRegion = 37
// Japan
Model_JP Model_HolidayRegion = 38
// Korea (South)
Model_KR Model_HolidayRegion = 39
// Latvia
Model_LV Model_HolidayRegion = 40
// Morocco
Model_MA Model_HolidayRegion = 41
// Mexico
Model_MX Model_HolidayRegion = 42
// Malaysia
Model_MY Model_HolidayRegion = 43
// Nigeria
Model_NG Model_HolidayRegion = 44
// Netherlands
Model_NL Model_HolidayRegion = 45
// Norway
Model_NO Model_HolidayRegion = 46
// New Zealand
Model_NZ Model_HolidayRegion = 47
// Peru
Model_PE Model_HolidayRegion = 48
// Philippines
Model_PH Model_HolidayRegion = 49
// Pakistan
Model_PK Model_HolidayRegion = 50
// Poland
Model_PL Model_HolidayRegion = 51
// Portugal
Model_PT Model_HolidayRegion = 52
// Romania
Model_RO Model_HolidayRegion = 53
// Serbia
Model_RS Model_HolidayRegion = 54
// Russian Federation
Model_RU Model_HolidayRegion = 55
// Saudi Arabia
Model_SA Model_HolidayRegion = 56
// Sweden
Model_SE Model_HolidayRegion = 57
// Singapore
Model_SG Model_HolidayRegion = 58
// Slovenia
Model_SI Model_HolidayRegion = 59
// Slovakia
Model_SK Model_HolidayRegion = 60
// Thailand
Model_TH Model_HolidayRegion = 61
// Turkey
Model_TR Model_HolidayRegion = 62
// Taiwan
Model_TW Model_HolidayRegion = 63
// Ukraine
Model_UA Model_HolidayRegion = 64
// United States
Model_US Model_HolidayRegion = 65
// Venezuela
Model_VE Model_HolidayRegion = 66
// Viet Nam
Model_VN Model_HolidayRegion = 67
// South Africa
Model_ZA Model_HolidayRegion = 68
)
func (Model_HolidayRegion) Descriptor() protoreflect.EnumDescriptor
func (x Model_HolidayRegion) Enum() *Model_HolidayRegion
func (Model_HolidayRegion) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_HolidayRegion.Descriptor instead.
func (x Model_HolidayRegion) Number() protoreflect.EnumNumber
func (x Model_HolidayRegion) String() string
func (Model_HolidayRegion) Type() protoreflect.EnumType
type Model_KmeansEnums struct {
// contains filtered or unexported fields
}
func (*Model_KmeansEnums) Descriptor() ([]byte, []int)
Deprecated: Use Model_KmeansEnums.ProtoReflect.Descriptor instead.
func (*Model_KmeansEnums) ProtoMessage()
func (x *Model_KmeansEnums) ProtoReflect() protoreflect.Message
func (x *Model_KmeansEnums) Reset()
func (x *Model_KmeansEnums) String() string
Indicates the method used to initialize the centroids for KMeans clustering algorithm.
type Model_KmeansEnums_KmeansInitializationMethod int32
const (
// Unspecified initialization method.
Model_KmeansEnums_KMEANS_INITIALIZATION_METHOD_UNSPECIFIED Model_KmeansEnums_KmeansInitializationMethod = 0
// Initializes the centroids randomly.
Model_KmeansEnums_RANDOM Model_KmeansEnums_KmeansInitializationMethod = 1
// Initializes the centroids using data specified in
// kmeans_initialization_column.
Model_KmeansEnums_CUSTOM Model_KmeansEnums_KmeansInitializationMethod = 2
// Initializes with kmeans++.
Model_KmeansEnums_KMEANS_PLUS_PLUS Model_KmeansEnums_KmeansInitializationMethod = 3
)
func (Model_KmeansEnums_KmeansInitializationMethod) Descriptor() protoreflect.EnumDescriptor
func (x Model_KmeansEnums_KmeansInitializationMethod) Enum() *Model_KmeansEnums_KmeansInitializationMethod
func (Model_KmeansEnums_KmeansInitializationMethod) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_KmeansEnums_KmeansInitializationMethod.Descriptor instead.
func (x Model_KmeansEnums_KmeansInitializationMethod) Number() protoreflect.EnumNumber
func (x Model_KmeansEnums_KmeansInitializationMethod) String() string
func (Model_KmeansEnums_KmeansInitializationMethod) Type() protoreflect.EnumType
Indicates the learning rate optimization strategy to use.
type Model_LearnRateStrategy int32
const (
Model_LEARN_RATE_STRATEGY_UNSPECIFIED Model_LearnRateStrategy = 0
// Use line search to determine learning rate.
Model_LINE_SEARCH Model_LearnRateStrategy = 1
// Use a constant learning rate.
Model_CONSTANT Model_LearnRateStrategy = 2
)
func (Model_LearnRateStrategy) Descriptor() protoreflect.EnumDescriptor
func (x Model_LearnRateStrategy) Enum() *Model_LearnRateStrategy
func (Model_LearnRateStrategy) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_LearnRateStrategy.Descriptor instead.
func (x Model_LearnRateStrategy) Number() protoreflect.EnumNumber
func (x Model_LearnRateStrategy) String() string
func (Model_LearnRateStrategy) Type() protoreflect.EnumType
Loss metric to evaluate model training performance.
type Model_LossType int32
const (
Model_LOSS_TYPE_UNSPECIFIED Model_LossType = 0
// Mean squared loss, used for linear regression.
Model_MEAN_SQUARED_LOSS Model_LossType = 1
// Mean log loss, used for logistic regression.
Model_MEAN_LOG_LOSS Model_LossType = 2
)
func (Model_LossType) Descriptor() protoreflect.EnumDescriptor
func (x Model_LossType) Enum() *Model_LossType
func (Model_LossType) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_LossType.Descriptor instead.
func (x Model_LossType) Number() protoreflect.EnumNumber
func (x Model_LossType) String() string
func (Model_LossType) Type() protoreflect.EnumType
Indicates the type of the Model.
type Model_ModelType int32
const (
Model_MODEL_TYPE_UNSPECIFIED Model_ModelType = 0
// Linear regression model.
Model_LINEAR_REGRESSION Model_ModelType = 1
// Logistic regression based classification model.
Model_LOGISTIC_REGRESSION Model_ModelType = 2
// K-means clustering model.
Model_KMEANS Model_ModelType = 3
// Matrix factorization model.
Model_MATRIX_FACTORIZATION Model_ModelType = 4
// DNN classifier model.
Model_DNN_CLASSIFIER Model_ModelType = 5
// An imported TensorFlow model.
Model_TENSORFLOW Model_ModelType = 6
// DNN regressor model.
Model_DNN_REGRESSOR Model_ModelType = 7
// Boosted tree regressor model.
Model_BOOSTED_TREE_REGRESSOR Model_ModelType = 9
// Boosted tree classifier model.
Model_BOOSTED_TREE_CLASSIFIER Model_ModelType = 10
// ARIMA model.
Model_ARIMA Model_ModelType = 11
// [Beta] AutoML Tables regression model.
Model_AUTOML_REGRESSOR Model_ModelType = 12
// [Beta] AutoML Tables classification model.
Model_AUTOML_CLASSIFIER Model_ModelType = 13
// New name for the ARIMA model.
Model_ARIMA_PLUS Model_ModelType = 19
)
func (Model_ModelType) Descriptor() protoreflect.EnumDescriptor
func (x Model_ModelType) Enum() *Model_ModelType
func (Model_ModelType) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_ModelType.Descriptor instead.
func (x Model_ModelType) Number() protoreflect.EnumNumber
func (x Model_ModelType) String() string
func (Model_ModelType) Type() protoreflect.EnumType
Evaluation metrics for multi-class classification/classifier models.
type Model_MultiClassClassificationMetrics struct {
// Aggregate classification metrics.
AggregateClassificationMetrics *Model_AggregateClassificationMetrics `protobuf:"bytes,1,opt,name=aggregate_classification_metrics,json=aggregateClassificationMetrics,proto3" json:"aggregate_classification_metrics,omitempty"`
// Confusion matrix at different thresholds.
ConfusionMatrixList []*Model_MultiClassClassificationMetrics_ConfusionMatrix `protobuf:"bytes,2,rep,name=confusion_matrix_list,json=confusionMatrixList,proto3" json:"confusion_matrix_list,omitempty"`
// contains filtered or unexported fields
}
func (*Model_MultiClassClassificationMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_MultiClassClassificationMetrics.ProtoReflect.Descriptor instead.
func (x *Model_MultiClassClassificationMetrics) GetAggregateClassificationMetrics() *Model_AggregateClassificationMetrics
func (x *Model_MultiClassClassificationMetrics) GetConfusionMatrixList() []*Model_MultiClassClassificationMetrics_ConfusionMatrix
func (*Model_MultiClassClassificationMetrics) ProtoMessage()
func (x *Model_MultiClassClassificationMetrics) ProtoReflect() protoreflect.Message
func (x *Model_MultiClassClassificationMetrics) Reset()
func (x *Model_MultiClassClassificationMetrics) String() string
Confusion matrix for multi-class classification models.
type Model_MultiClassClassificationMetrics_ConfusionMatrix struct {
// Confidence threshold used when computing the entries of the
// confusion matrix.
ConfidenceThreshold *wrapperspb.DoubleValue `protobuf:"bytes,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
// One row per actual label.
Rows []*Model_MultiClassClassificationMetrics_ConfusionMatrix_Row `protobuf:"bytes,2,rep,name=rows,proto3" json:"rows,omitempty"`
// contains filtered or unexported fields
}
func (*Model_MultiClassClassificationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int)
Deprecated: Use Model_MultiClassClassificationMetrics_ConfusionMatrix.ProtoReflect.Descriptor instead.
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix) GetConfidenceThreshold() *wrapperspb.DoubleValue
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix) GetRows() []*Model_MultiClassClassificationMetrics_ConfusionMatrix_Row
func (*Model_MultiClassClassificationMetrics_ConfusionMatrix) ProtoMessage()
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix) ProtoReflect() protoreflect.Message
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix) Reset()
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix) String() string
A single entry in the confusion matrix.
type Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry struct {
// The predicted label. For confidence_threshold > 0, we will
// also add an entry indicating the number of items under the
// confidence threshold.
PredictedLabel string `protobuf:"bytes,1,opt,name=predicted_label,json=predictedLabel,proto3" json:"predicted_label,omitempty"`
// Number of items being predicted as this label.
ItemCount *wrapperspb.Int64Value `protobuf:"bytes,2,opt,name=item_count,json=itemCount,proto3" json:"item_count,omitempty"`
// contains filtered or unexported fields
}
func (*Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) Descriptor() ([]byte, []int)
Deprecated: Use Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry.ProtoReflect.Descriptor instead.
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) GetItemCount() *wrapperspb.Int64Value
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) GetPredictedLabel() string
func (*Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) ProtoMessage()
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) ProtoReflect() protoreflect.Message
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) Reset()
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) String() string
A single row in the confusion matrix.
type Model_MultiClassClassificationMetrics_ConfusionMatrix_Row struct {
// The original label of this row.
ActualLabel string `protobuf:"bytes,1,opt,name=actual_label,json=actualLabel,proto3" json:"actual_label,omitempty"`
// Info describing predicted label distribution.
Entries []*Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry `protobuf:"bytes,2,rep,name=entries,proto3" json:"entries,omitempty"`
// contains filtered or unexported fields
}
func (*Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int)
Deprecated: Use Model_MultiClassClassificationMetrics_ConfusionMatrix_Row.ProtoReflect.Descriptor instead.
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) GetActualLabel() string
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) GetEntries() []*Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry
func (*Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) ProtoMessage()
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) ProtoReflect() protoreflect.Message
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) Reset()
func (x *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) String() string
Indicates the optimization strategy used for training.
type Model_OptimizationStrategy int32
const (
Model_OPTIMIZATION_STRATEGY_UNSPECIFIED Model_OptimizationStrategy = 0
// Uses an iterative batch gradient descent algorithm.
Model_BATCH_GRADIENT_DESCENT Model_OptimizationStrategy = 1
// Uses a normal equation to solve linear regression problem.
Model_NORMAL_EQUATION Model_OptimizationStrategy = 2
)
func (Model_OptimizationStrategy) Descriptor() protoreflect.EnumDescriptor
func (x Model_OptimizationStrategy) Enum() *Model_OptimizationStrategy
func (Model_OptimizationStrategy) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_OptimizationStrategy.Descriptor instead.
func (x Model_OptimizationStrategy) Number() protoreflect.EnumNumber
func (x Model_OptimizationStrategy) String() string
func (Model_OptimizationStrategy) Type() protoreflect.EnumType
Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.
type Model_RankingMetrics struct {
// Calculates a precision per user for all the items by ranking them and
// then averages all the precisions across all the users.
MeanAveragePrecision *wrapperspb.DoubleValue `protobuf:"bytes,1,opt,name=mean_average_precision,json=meanAveragePrecision,proto3" json:"mean_average_precision,omitempty"`
// Similar to the mean squared error computed in regression and explicit
// recommendation models except instead of computing the rating directly,
// the output from evaluate is computed against a preference which is 1 or 0
// depending on if the rating exists or not.
MeanSquaredError *wrapperspb.DoubleValue `protobuf:"bytes,2,opt,name=mean_squared_error,json=meanSquaredError,proto3" json:"mean_squared_error,omitempty"`
// A metric to determine the goodness of a ranking calculated from the
// predicted confidence by comparing it to an ideal rank measured by the
// original ratings.
NormalizedDiscountedCumulativeGain *wrapperspb.DoubleValue `protobuf:"bytes,3,opt,name=normalized_discounted_cumulative_gain,json=normalizedDiscountedCumulativeGain,proto3" json:"normalized_discounted_cumulative_gain,omitempty"`
// Determines the goodness of a ranking by computing the percentile rank
// from the predicted confidence and dividing it by the original rank.
AverageRank *wrapperspb.DoubleValue `protobuf:"bytes,4,opt,name=average_rank,json=averageRank,proto3" json:"average_rank,omitempty"`
// contains filtered or unexported fields
}
func (*Model_RankingMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_RankingMetrics.ProtoReflect.Descriptor instead.
func (x *Model_RankingMetrics) GetAverageRank() *wrapperspb.DoubleValue
func (x *Model_RankingMetrics) GetMeanAveragePrecision() *wrapperspb.DoubleValue
func (x *Model_RankingMetrics) GetMeanSquaredError() *wrapperspb.DoubleValue
func (x *Model_RankingMetrics) GetNormalizedDiscountedCumulativeGain() *wrapperspb.DoubleValue
func (*Model_RankingMetrics) ProtoMessage()
func (x *Model_RankingMetrics) ProtoReflect() protoreflect.Message
func (x *Model_RankingMetrics) Reset()
func (x *Model_RankingMetrics) String() string
Evaluation metrics for regression and explicit feedback type matrix factorization models.
type Model_RegressionMetrics struct {
// Mean absolute error.
MeanAbsoluteError *wrapperspb.DoubleValue `protobuf:"bytes,1,opt,name=mean_absolute_error,json=meanAbsoluteError,proto3" json:"mean_absolute_error,omitempty"`
// Mean squared error.
MeanSquaredError *wrapperspb.DoubleValue `protobuf:"bytes,2,opt,name=mean_squared_error,json=meanSquaredError,proto3" json:"mean_squared_error,omitempty"`
// Mean squared log error.
MeanSquaredLogError *wrapperspb.DoubleValue `protobuf:"bytes,3,opt,name=mean_squared_log_error,json=meanSquaredLogError,proto3" json:"mean_squared_log_error,omitempty"`
// Median absolute error.
MedianAbsoluteError *wrapperspb.DoubleValue `protobuf:"bytes,4,opt,name=median_absolute_error,json=medianAbsoluteError,proto3" json:"median_absolute_error,omitempty"`
// R^2 score. This corresponds to r2_score in ML.EVALUATE.
RSquared *wrapperspb.DoubleValue `protobuf:"bytes,5,opt,name=r_squared,json=rSquared,proto3" json:"r_squared,omitempty"`
// contains filtered or unexported fields
}
func (*Model_RegressionMetrics) Descriptor() ([]byte, []int)
Deprecated: Use Model_RegressionMetrics.ProtoReflect.Descriptor instead.
func (x *Model_RegressionMetrics) GetMeanAbsoluteError() *wrapperspb.DoubleValue
func (x *Model_RegressionMetrics) GetMeanSquaredError() *wrapperspb.DoubleValue
func (x *Model_RegressionMetrics) GetMeanSquaredLogError() *wrapperspb.DoubleValue
func (x *Model_RegressionMetrics) GetMedianAbsoluteError() *wrapperspb.DoubleValue
func (x *Model_RegressionMetrics) GetRSquared() *wrapperspb.DoubleValue
func (*Model_RegressionMetrics) ProtoMessage()
func (x *Model_RegressionMetrics) ProtoReflect() protoreflect.Message
func (x *Model_RegressionMetrics) Reset()
func (x *Model_RegressionMetrics) String() string
type Model_SeasonalPeriod struct {
// contains filtered or unexported fields
}
func (*Model_SeasonalPeriod) Descriptor() ([]byte, []int)
Deprecated: Use Model_SeasonalPeriod.ProtoReflect.Descriptor instead.
func (*Model_SeasonalPeriod) ProtoMessage()
func (x *Model_SeasonalPeriod) ProtoReflect() protoreflect.Message
func (x *Model_SeasonalPeriod) Reset()
func (x *Model_SeasonalPeriod) String() string
type Model_SeasonalPeriod_SeasonalPeriodType int32
const (
Model_SeasonalPeriod_SEASONAL_PERIOD_TYPE_UNSPECIFIED Model_SeasonalPeriod_SeasonalPeriodType = 0
// No seasonality
Model_SeasonalPeriod_NO_SEASONALITY Model_SeasonalPeriod_SeasonalPeriodType = 1
// Daily period, 24 hours.
Model_SeasonalPeriod_DAILY Model_SeasonalPeriod_SeasonalPeriodType = 2
// Weekly period, 7 days.
Model_SeasonalPeriod_WEEKLY Model_SeasonalPeriod_SeasonalPeriodType = 3
// Monthly period, 30 days or irregular.
Model_SeasonalPeriod_MONTHLY Model_SeasonalPeriod_SeasonalPeriodType = 4
// Quarterly period, 90 days or irregular.
Model_SeasonalPeriod_QUARTERLY Model_SeasonalPeriod_SeasonalPeriodType = 5
// Yearly period, 365 days or irregular.
Model_SeasonalPeriod_YEARLY Model_SeasonalPeriod_SeasonalPeriodType = 6
)
func (Model_SeasonalPeriod_SeasonalPeriodType) Descriptor() protoreflect.EnumDescriptor
func (x Model_SeasonalPeriod_SeasonalPeriodType) Enum() *Model_SeasonalPeriod_SeasonalPeriodType
func (Model_SeasonalPeriod_SeasonalPeriodType) EnumDescriptor() ([]byte, []int)
Deprecated: Use Model_SeasonalPeriod_SeasonalPeriodType.Descriptor instead.
func (x Model_SeasonalPeriod_SeasonalPeriodType) Number() protoreflect.EnumNumber
func (x Model_SeasonalPeriod_SeasonalPeriodType) String() string
func (Model_SeasonalPeriod_SeasonalPeriodType) Type() protoreflect.EnumType
Information about a single training query run for the model.
type Model_TrainingRun struct {
// Options that were used for this training run, includes
// user specified and default options that were used.
TrainingOptions *Model_TrainingRun_TrainingOptions `protobuf:"bytes,1,opt,name=training_options,json=trainingOptions,proto3" json:"training_options,omitempty"`
// The start time of this training run.
StartTime *timestamppb.Timestamp `protobuf:"bytes,8,opt,name=start_time,json=startTime,proto3" json:"start_time,omitempty"`
// Output of each iteration run, results.size() <= max_iterations.
Results []*Model_TrainingRun_IterationResult `protobuf:"bytes,6,rep,name=results,proto3" json:"results,omitempty"`
// The evaluation metrics over training/eval data that were computed at the
// end of training.
EvaluationMetrics *Model_EvaluationMetrics `protobuf:"bytes,7,opt,name=evaluation_metrics,json=evaluationMetrics,proto3" json:"evaluation_metrics,omitempty"`
// Data split result of the training run. Only set when the input data is
// actually split.
DataSplitResult *Model_DataSplitResult `protobuf:"bytes,9,opt,name=data_split_result,json=dataSplitResult,proto3" json:"data_split_result,omitempty"`
// Global explanations for important features of the model. For multi-class
// models, there is one entry for each label class. For other models, there
// is only one entry in the list.
GlobalExplanations []*Model_GlobalExplanation `protobuf:"bytes,10,rep,name=global_explanations,json=globalExplanations,proto3" json:"global_explanations,omitempty"`
// contains filtered or unexported fields
}
func (*Model_TrainingRun) Descriptor() ([]byte, []int)
Deprecated: Use Model_TrainingRun.ProtoReflect.Descriptor instead.
func (x *Model_TrainingRun) GetDataSplitResult() *Model_DataSplitResult
func (x *Model_TrainingRun) GetEvaluationMetrics() *Model_EvaluationMetrics
func (x *Model_TrainingRun) GetGlobalExplanations() []*Model_GlobalExplanation
func (x *Model_TrainingRun) GetResults() []*Model_TrainingRun_IterationResult
func (x *Model_TrainingRun) GetStartTime() *timestamppb.Timestamp
func (x *Model_TrainingRun) GetTrainingOptions() *Model_TrainingRun_TrainingOptions
func (*Model_TrainingRun) ProtoMessage()
func (x *Model_TrainingRun) ProtoReflect() protoreflect.Message
func (x *Model_TrainingRun) Reset()
func (x *Model_TrainingRun) String() string
Information about a single iteration of the training run.
type Model_TrainingRun_IterationResult struct {
// Index of the iteration, 0 based.
Index *wrapperspb.Int32Value `protobuf:"bytes,1,opt,name=index,proto3" json:"index,omitempty"`
// Time taken to run the iteration in milliseconds.
DurationMs *wrapperspb.Int64Value `protobuf:"bytes,4,opt,name=duration_ms,json=durationMs,proto3" json:"duration_ms,omitempty"`
// Loss computed on the training data at the end of iteration.
TrainingLoss *wrapperspb.DoubleValue `protobuf:"bytes,5,opt,name=training_loss,json=trainingLoss,proto3" json:"training_loss,omitempty"`
// Loss computed on the eval data at the end of iteration.
EvalLoss *wrapperspb.DoubleValue `protobuf:"bytes,6,opt,name=eval_loss,json=evalLoss,proto3" json:"eval_loss,omitempty"`
// Learn rate used for this iteration.
LearnRate float64 `protobuf:"fixed64,7,opt,name=learn_rate,json=learnRate,proto3" json:"learn_rate,omitempty"`
// Information about top clusters for clustering models.
ClusterInfos []*Model_TrainingRun_IterationResult_ClusterInfo `protobuf:"bytes,8,rep,name=cluster_infos,json=clusterInfos,proto3" json:"cluster_infos,omitempty"`
ArimaResult *Model_TrainingRun_IterationResult_ArimaResult `protobuf:"bytes,9,opt,name=arima_result,json=arimaResult,proto3" json:"arima_result,omitempty"`
// contains filtered or unexported fields
}
func (*Model_TrainingRun_IterationResult) Descriptor() ([]byte, []int)
Deprecated: Use Model_TrainingRun_IterationResult.ProtoReflect.Descriptor instead.
func (x *Model_TrainingRun_IterationResult) GetArimaResult() *Model_TrainingRun_IterationResult_ArimaResult
func (x *Model_TrainingRun_IterationResult) GetClusterInfos() []*Model_TrainingRun_IterationResult_ClusterInfo
func (x *Model_TrainingRun_IterationResult) GetDurationMs() *wrapperspb.Int64Value
func (x *Model_TrainingRun_IterationResult) GetEvalLoss() *wrapperspb.DoubleValue
func (x *Model_TrainingRun_IterationResult) GetIndex() *wrapperspb.Int32Value
func (x *Model_TrainingRun_IterationResult) GetLearnRate() float64
func (x *Model_TrainingRun_IterationResult) GetTrainingLoss() *wrapperspb.DoubleValue
func (*Model_TrainingRun_IterationResult) ProtoMessage()
func (x *Model_TrainingRun_IterationResult) ProtoReflect() protoreflect.Message
func (x *Model_TrainingRun_IterationResult) Reset()
func (x *Model_TrainingRun_IterationResult) String() string
(Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results.
type Model_TrainingRun_IterationResult_ArimaResult struct {
// This message is repeated because there are multiple arima models
// fitted in auto-arima. For non-auto-arima model, its size is one.
ArimaModelInfo []*Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo `protobuf:"bytes,1,rep,name=arima_model_info,json=arimaModelInfo,proto3" json:"arima_model_info,omitempty"`
// Seasonal periods. Repeated because multiple periods are supported for
// one time series.
SeasonalPeriods []Model_SeasonalPeriod_SeasonalPeriodType `protobuf:"varint,2,rep,packed,name=seasonal_periods,json=seasonalPeriods,proto3,enum=google.cloud.bigquery.v2.Model_SeasonalPeriod_SeasonalPeriodType" json:"seasonal_periods,omitempty"`
// contains filtered or unexported fields
}
func (*Model_TrainingRun_IterationResult_ArimaResult) Descriptor() ([]byte, []int)
Deprecated: Use Model_TrainingRun_IterationResult_ArimaResult.ProtoReflect.Descriptor instead.
func (x *Model_TrainingRun_IterationResult_ArimaResult) GetArimaModelInfo() []*Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo
func (x *Model_TrainingRun_IterationResult_ArimaResult) GetSeasonalPeriods() []Model_SeasonalPeriod_SeasonalPeriodType
func (*Model_TrainingRun_IterationResult_ArimaResult) ProtoMessage()
func (x *Model_TrainingRun_IterationResult_ArimaResult) ProtoReflect() protoreflect.Message
func (x *Model_TrainingRun_IterationResult_ArimaResult) Reset()
func (x *Model_TrainingRun_IterationResult_ArimaResult) String() string
Arima coefficients.
type Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients struct {
// Auto-regressive coefficients, an array of double.
AutoRegressiveCoefficients []float64 `protobuf:"fixed64,1,rep,packed,name=auto_regressive_coefficients,json=autoRegressiveCoefficients,proto3" json:"auto_regressive_coefficients,omitempty"`
// Moving-average coefficients, an array of double.
MovingAverageCoefficients []float64 `protobuf:"fixed64,2,rep,packed,name=moving_average_coefficients,json=movingAverageCoefficients,proto3" json:"moving_average_coefficients,omitempty"`
// Intercept coefficient, just a double not an array.
InterceptCoefficient float64 `protobuf:"fixed64,3,opt,name=intercept_coefficient,json=interceptCoefficient,proto3" json:"intercept_coefficient,omitempty"`
// contains filtered or unexported fields
}
func (*Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients) Descriptor() ([]byte, []int)
Deprecated: Use Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients.ProtoReflect.Descriptor instead.
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients) GetAutoRegressiveCoefficients() []float64
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients) GetInterceptCoefficient() float64
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients) GetMovingAverageCoefficients() []float64
func (*Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients) ProtoMessage()
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients) ProtoReflect() protoreflect.Message
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients) Reset()
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients) String() string
Arima model information.
type Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo struct {
// Non-seasonal order.
NonSeasonalOrder *Model_ArimaOrder `protobuf:"bytes,1,opt,name=non_seasonal_order,json=nonSeasonalOrder,proto3" json:"non_seasonal_order,omitempty"`
// Arima coefficients.
ArimaCoefficients *Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients `protobuf:"bytes,2,opt,name=arima_coefficients,json=arimaCoefficients,proto3" json:"arima_coefficients,omitempty"`
// Arima fitting metrics.
ArimaFittingMetrics *Model_ArimaFittingMetrics `protobuf:"bytes,3,opt,name=arima_fitting_metrics,json=arimaFittingMetrics,proto3" json:"arima_fitting_metrics,omitempty"`
// Whether Arima model fitted with drift or not. It is always false
// when d is not 1.
HasDrift bool `protobuf:"varint,4,opt,name=has_drift,json=hasDrift,proto3" json:"has_drift,omitempty"`
// The time_series_id value for this time series. It will be one of
// the unique values from the time_series_id_column specified during
// ARIMA model training. Only present when time_series_id_column
// training option was used.
TimeSeriesId string `protobuf:"bytes,5,opt,name=time_series_id,json=timeSeriesId,proto3" json:"time_series_id,omitempty"`
// The tuple of time_series_ids identifying this time series. It will
// be one of the unique tuples of values present in the
// time_series_id_columns specified during ARIMA model training. Only
// present when time_series_id_columns training option was used and
// the order of values here are same as the order of
// time_series_id_columns.
TimeSeriesIds []string `protobuf:"bytes,10,rep,name=time_series_ids,json=timeSeriesIds,proto3" json:"time_series_ids,omitempty"`
// Seasonal periods. Repeated because multiple periods are supported
// for one time series.
SeasonalPeriods []Model_SeasonalPeriod_SeasonalPeriodType `protobuf:"varint,6,rep,packed,name=seasonal_periods,json=seasonalPeriods,proto3,enum=google.cloud.bigquery.v2.Model_SeasonalPeriod_SeasonalPeriodType" json:"seasonal_periods,omitempty"`
// If true, holiday_effect is a part of time series decomposition
// result.
HasHolidayEffect *wrapperspb.BoolValue `protobuf:"bytes,7,opt,name=has_holiday_effect,json=hasHolidayEffect,proto3" json:"has_holiday_effect,omitempty"`
// If true, spikes_and_dips is a part of time series decomposition
// result.
HasSpikesAndDips *wrapperspb.BoolValue `protobuf:"bytes,8,opt,name=has_spikes_and_dips,json=hasSpikesAndDips,proto3" json:"has_spikes_and_dips,omitempty"`
// If true, step_changes is a part of time series decomposition
// result.
HasStepChanges *wrapperspb.BoolValue `protobuf:"bytes,9,opt,name=has_step_changes,json=hasStepChanges,proto3" json:"has_step_changes,omitempty"`
// contains filtered or unexported fields
}
func (*Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) Descriptor() ([]byte, []int)
Deprecated: Use Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo.ProtoReflect.Descriptor instead.
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetArimaCoefficients() *Model_TrainingRun_IterationResult_ArimaResult_ArimaCoefficients
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetArimaFittingMetrics() *Model_ArimaFittingMetrics
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetHasDrift() bool
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetHasHolidayEffect() *wrapperspb.BoolValue
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetHasSpikesAndDips() *wrapperspb.BoolValue
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetHasStepChanges() *wrapperspb.BoolValue
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetNonSeasonalOrder() *Model_ArimaOrder
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetSeasonalPeriods() []Model_SeasonalPeriod_SeasonalPeriodType
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetTimeSeriesId() string
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) GetTimeSeriesIds() []string
func (*Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) ProtoMessage()
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) ProtoReflect() protoreflect.Message
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) Reset()
func (x *Model_TrainingRun_IterationResult_ArimaResult_ArimaModelInfo) String() string
Information about a single cluster for clustering model.
type Model_TrainingRun_IterationResult_ClusterInfo struct {
// Centroid id.
CentroidId int64 `protobuf:"varint,1,opt,name=centroid_id,json=centroidId,proto3" json:"centroid_id,omitempty"`
// Cluster radius, the average distance from centroid
// to each point assigned to the cluster.
ClusterRadius *wrapperspb.DoubleValue `protobuf:"bytes,2,opt,name=cluster_radius,json=clusterRadius,proto3" json:"cluster_radius,omitempty"`
// Cluster size, the total number of points assigned to the cluster.
ClusterSize *wrapperspb.Int64Value `protobuf:"bytes,3,opt,name=cluster_size,json=clusterSize,proto3" json:"cluster_size,omitempty"`
// contains filtered or unexported fields
}
func (*Model_TrainingRun_IterationResult_ClusterInfo) Descriptor() ([]byte, []int)
Deprecated: Use Model_TrainingRun_IterationResult_ClusterInfo.ProtoReflect.Descriptor instead.
func (x *Model_TrainingRun_IterationResult_ClusterInfo) GetCentroidId() int64
func (x *Model_TrainingRun_IterationResult_ClusterInfo) GetClusterRadius() *wrapperspb.DoubleValue
func (x *Model_TrainingRun_IterationResult_ClusterInfo) GetClusterSize() *wrapperspb.Int64Value
func (*Model_TrainingRun_IterationResult_ClusterInfo) ProtoMessage()
func (x *Model_TrainingRun_IterationResult_ClusterInfo) ProtoReflect() protoreflect.Message
func (x *Model_TrainingRun_IterationResult_ClusterInfo) Reset()
func (x *Model_TrainingRun_IterationResult_ClusterInfo) String() string
Options used in model training.
type Model_TrainingRun_TrainingOptions struct {
// The maximum number of iterations in training. Used only for iterative
// training algorithms.
MaxIterations int64 `protobuf:"varint,1,opt,name=max_iterations,json=maxIterations,proto3" json:"max_iterations,omitempty"`
// Type of loss function used during training run.
LossType Model_LossType `protobuf:"varint,2,opt,name=loss_type,json=lossType,proto3,enum=google.cloud.bigquery.v2.Model_LossType" json:"loss_type,omitempty"`
// Learning rate in training. Used only for iterative training algorithms.
LearnRate float64 `protobuf:"fixed64,3,opt,name=learn_rate,json=learnRate,proto3" json:"learn_rate,omitempty"`
// L1 regularization coefficient.
L1Regularization *wrapperspb.DoubleValue `protobuf:"bytes,4,opt,name=l1_regularization,json=l1Regularization,proto3" json:"l1_regularization,omitempty"`
// L2 regularization coefficient.
L2Regularization *wrapperspb.DoubleValue `protobuf:"bytes,5,opt,name=l2_regularization,json=l2Regularization,proto3" json:"l2_regularization,omitempty"`
// When early_stop is true, stops training when accuracy improvement is
// less than 'min_relative_progress'. Used only for iterative training
// algorithms.
MinRelativeProgress *wrapperspb.DoubleValue `protobuf:"bytes,6,opt,name=min_relative_progress,json=minRelativeProgress,proto3" json:"min_relative_progress,omitempty"`
// Whether to train a model from the last checkpoint.
WarmStart *wrapperspb.BoolValue `protobuf:"bytes,7,opt,name=warm_start,json=warmStart,proto3" json:"warm_start,omitempty"`
// Whether to stop early when the loss doesn't improve significantly
// any more (compared to min_relative_progress). Used only for iterative
// training algorithms.
EarlyStop *wrapperspb.BoolValue `protobuf:"bytes,8,opt,name=early_stop,json=earlyStop,proto3" json:"early_stop,omitempty"`
// Name of input label columns in training data.
InputLabelColumns []string `protobuf:"bytes,9,rep,name=input_label_columns,json=inputLabelColumns,proto3" json:"input_label_columns,omitempty"`
// The data split type for training and evaluation, e.g. RANDOM.
DataSplitMethod Model_DataSplitMethod `protobuf:"varint,10,opt,name=data_split_method,json=dataSplitMethod,proto3,enum=google.cloud.bigquery.v2.Model_DataSplitMethod" json:"data_split_method,omitempty"`
// The fraction of evaluation data over the whole input data. The rest
// of data will be used as training data. The format should be double.
// Accurate to two decimal places.
// Default value is 0.2.
DataSplitEvalFraction float64 `protobuf:"fixed64,11,opt,name=data_split_eval_fraction,json=dataSplitEvalFraction,proto3" json:"data_split_eval_fraction,omitempty"`
// The column to split data with. This column won't be used as a
// feature.
// 1. When data_split_method is CUSTOM, the corresponding column should
// be boolean. The rows with true value tag are eval data, and the false
// are training data.
// 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION
// rows (from smallest to largest) in the corresponding column are used
// as training data, and the rest are eval data. It respects the order
// in Orderable data types:
// https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
DataSplitColumn string `protobuf:"bytes,12,opt,name=data_split_column,json=dataSplitColumn,proto3" json:"data_split_column,omitempty"`
// The strategy to determine learn rate for the current iteration.
LearnRateStrategy Model_LearnRateStrategy `protobuf:"varint,13,opt,name=learn_rate_strategy,json=learnRateStrategy,proto3,enum=google.cloud.bigquery.v2.Model_LearnRateStrategy" json:"learn_rate_strategy,omitempty"`
// Specifies the initial learning rate for the line search learn rate
// strategy.
InitialLearnRate float64 `protobuf:"fixed64,16,opt,name=initial_learn_rate,json=initialLearnRate,proto3" json:"initial_learn_rate,omitempty"`
// Weights associated with each label class, for rebalancing the
// training data. Only applicable for classification models.
LabelClassWeights map[string]float64 `protobuf:"bytes,17,rep,name=label_class_weights,json=labelClassWeights,proto3" json:"label_class_weights,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"fixed64,2,opt,name=value,proto3"`
// User column specified for matrix factorization models.
UserColumn string `protobuf:"bytes,18,opt,name=user_column,json=userColumn,proto3" json:"user_column,omitempty"`
// Item column specified for matrix factorization models.
ItemColumn string `protobuf:"bytes,19,opt,name=item_column,json=itemColumn,proto3" json:"item_column,omitempty"`
// Distance type for clustering models.
DistanceType Model_DistanceType `protobuf:"varint,20,opt,name=distance_type,json=distanceType,proto3,enum=google.cloud.bigquery.v2.Model_DistanceType" json:"distance_type,omitempty"`
// Number of clusters for clustering models.
NumClusters int64 `protobuf:"varint,21,opt,name=num_clusters,json=numClusters,proto3" json:"num_clusters,omitempty"`
// Google Cloud Storage URI from which the model was imported. Only
// applicable for imported models.
ModelUri string `protobuf:"bytes,22,opt,name=model_uri,json=modelUri,proto3" json:"model_uri,omitempty"`
// Optimization strategy for training linear regression models.
OptimizationStrategy Model_OptimizationStrategy `protobuf:"varint,23,opt,name=optimization_strategy,json=optimizationStrategy,proto3,enum=google.cloud.bigquery.v2.Model_OptimizationStrategy" json:"optimization_strategy,omitempty"`
// Hidden units for dnn models.
HiddenUnits []int64 `protobuf:"varint,24,rep,packed,name=hidden_units,json=hiddenUnits,proto3" json:"hidden_units,omitempty"`
// Batch size for dnn models.
BatchSize int64 `protobuf:"varint,25,opt,name=batch_size,json=batchSize,proto3" json:"batch_size,omitempty"`
// Dropout probability for dnn models.
Dropout *wrapperspb.DoubleValue `protobuf:"bytes,26,opt,name=dropout,proto3" json:"dropout,omitempty"`
// Maximum depth of a tree for boosted tree models.
MaxTreeDepth int64 `protobuf:"varint,27,opt,name=max_tree_depth,json=maxTreeDepth,proto3" json:"max_tree_depth,omitempty"`
// Subsample fraction of the training data to grow tree to prevent
// overfitting for boosted tree models.
Subsample float64 `protobuf:"fixed64,28,opt,name=subsample,proto3" json:"subsample,omitempty"`
// Minimum split loss for boosted tree models.
MinSplitLoss *wrapperspb.DoubleValue `protobuf:"bytes,29,opt,name=min_split_loss,json=minSplitLoss,proto3" json:"min_split_loss,omitempty"`
// Num factors specified for matrix factorization models.
NumFactors int64 `protobuf:"varint,30,opt,name=num_factors,json=numFactors,proto3" json:"num_factors,omitempty"`
// Feedback type that specifies which algorithm to run for matrix
// factorization.
FeedbackType Model_FeedbackType `protobuf:"varint,31,opt,name=feedback_type,json=feedbackType,proto3,enum=google.cloud.bigquery.v2.Model_FeedbackType" json:"feedback_type,omitempty"`
// Hyperparameter for matrix factoration when implicit feedback type is
// specified.
WalsAlpha *wrapperspb.DoubleValue `protobuf:"bytes,32,opt,name=wals_alpha,json=walsAlpha,proto3" json:"wals_alpha,omitempty"`
// The method used to initialize the centroids for kmeans algorithm.
KmeansInitializationMethod Model_KmeansEnums_KmeansInitializationMethod `protobuf:"varint,33,opt,name=kmeans_initialization_method,json=kmeansInitializationMethod,proto3,enum=google.cloud.bigquery.v2.Model_KmeansEnums_KmeansInitializationMethod" json:"kmeans_initialization_method,omitempty"`
// The column used to provide the initial centroids for kmeans algorithm
// when kmeans_initialization_method is CUSTOM.
KmeansInitializationColumn string `protobuf:"bytes,34,opt,name=kmeans_initialization_column,json=kmeansInitializationColumn,proto3" json:"kmeans_initialization_column,omitempty"`
// Column to be designated as time series timestamp for ARIMA model.
TimeSeriesTimestampColumn string `protobuf:"bytes,35,opt,name=time_series_timestamp_column,json=timeSeriesTimestampColumn,proto3" json:"time_series_timestamp_column,omitempty"`
// Column to be designated as time series data for ARIMA model.
TimeSeriesDataColumn string `protobuf:"bytes,36,opt,name=time_series_data_column,json=timeSeriesDataColumn,proto3" json:"time_series_data_column,omitempty"`
// Whether to enable auto ARIMA or not.
AutoArima bool `protobuf:"varint,37,opt,name=auto_arima,json=autoArima,proto3" json:"auto_arima,omitempty"`
// A specification of the non-seasonal part of the ARIMA model: the three
// components (p, d, q) are the AR order, the degree of differencing, and
// the MA order.
NonSeasonalOrder *Model_ArimaOrder `protobuf:"bytes,38,opt,name=non_seasonal_order,json=nonSeasonalOrder,proto3" json:"non_seasonal_order,omitempty"`
// The data frequency of a time series.
DataFrequency Model_DataFrequency `protobuf:"varint,39,opt,name=data_frequency,json=dataFrequency,proto3,enum=google.cloud.bigquery.v2.Model_DataFrequency" json:"data_frequency,omitempty"`
// Include drift when fitting an ARIMA model.
IncludeDrift bool `protobuf:"varint,41,opt,name=include_drift,json=includeDrift,proto3" json:"include_drift,omitempty"`
// The geographical region based on which the holidays are considered in
// time series modeling. If a valid value is specified, then holiday
// effects modeling is enabled.
HolidayRegion Model_HolidayRegion `protobuf:"varint,42,opt,name=holiday_region,json=holidayRegion,proto3,enum=google.cloud.bigquery.v2.Model_HolidayRegion" json:"holiday_region,omitempty"`
// The time series id column that was used during ARIMA model training.
TimeSeriesIdColumn string `protobuf:"bytes,43,opt,name=time_series_id_column,json=timeSeriesIdColumn,proto3" json:"time_series_id_column,omitempty"`
// The time series id columns that were used during ARIMA model training.
TimeSeriesIdColumns []string `protobuf:"bytes,51,rep,name=time_series_id_columns,json=timeSeriesIdColumns,proto3" json:"time_series_id_columns,omitempty"`
// The number of periods ahead that need to be forecasted.
Horizon int64 `protobuf:"varint,44,opt,name=horizon,proto3" json:"horizon,omitempty"`
// Whether to preserve the input structs in output feature names.
// Suppose there is a struct A with field b.
// When false (default), the output feature name is A_b.
// When true, the output feature name is A.b.
PreserveInputStructs bool `protobuf:"varint,45,opt,name=preserve_input_structs,json=preserveInputStructs,proto3" json:"preserve_input_structs,omitempty"`
// The max value of non-seasonal p and q.
AutoArimaMaxOrder int64 `protobuf:"varint,46,opt,name=auto_arima_max_order,json=autoArimaMaxOrder,proto3" json:"auto_arima_max_order,omitempty"`
// If true, perform decompose time series and save the results.
DecomposeTimeSeries *wrapperspb.BoolValue `protobuf:"bytes,50,opt,name=decompose_time_series,json=decomposeTimeSeries,proto3" json:"decompose_time_series,omitempty"`
// If true, clean spikes and dips in the input time series.
CleanSpikesAndDips *wrapperspb.BoolValue `protobuf:"bytes,52,opt,name=clean_spikes_and_dips,json=cleanSpikesAndDips,proto3" json:"clean_spikes_and_dips,omitempty"`
// If true, detect step changes and make data adjustment in the input time
// series.
AdjustStepChanges *wrapperspb.BoolValue `protobuf:"bytes,53,opt,name=adjust_step_changes,json=adjustStepChanges,proto3" json:"adjust_step_changes,omitempty"`
// contains filtered or unexported fields
}
func (*Model_TrainingRun_TrainingOptions) Descriptor() ([]byte, []int)
Deprecated: Use Model_TrainingRun_TrainingOptions.ProtoReflect.Descriptor instead.
func (x *Model_TrainingRun_TrainingOptions) GetAdjustStepChanges() *wrapperspb.BoolValue
func (x *Model_TrainingRun_TrainingOptions) GetAutoArima() bool
func (x *Model_TrainingRun_TrainingOptions) GetAutoArimaMaxOrder() int64
func (x *Model_TrainingRun_TrainingOptions) GetBatchSize() int64
func (x *Model_TrainingRun_TrainingOptions) GetCleanSpikesAndDips() *wrapperspb.BoolValue
func (x *Model_TrainingRun_TrainingOptions) GetDataFrequency() Model_DataFrequency
func (x *Model_TrainingRun_TrainingOptions) GetDataSplitColumn() string
func (x *Model_TrainingRun_TrainingOptions) GetDataSplitEvalFraction() float64
func (x *Model_TrainingRun_TrainingOptions) GetDataSplitMethod() Model_DataSplitMethod
func (x *Model_TrainingRun_TrainingOptions) GetDecomposeTimeSeries() *wrapperspb.BoolValue
func (x *Model_TrainingRun_TrainingOptions) GetDistanceType() Model_DistanceType
func (x *Model_TrainingRun_TrainingOptions) GetDropout() *wrapperspb.DoubleValue
func (x *Model_TrainingRun_TrainingOptions) GetEarlyStop() *wrapperspb.BoolValue
func (x *Model_TrainingRun_TrainingOptions) GetFeedbackType() Model_FeedbackType
func (x *Model_TrainingRun_TrainingOptions) GetHiddenUnits() []int64
func (x *Model_TrainingRun_TrainingOptions) GetHolidayRegion() Model_HolidayRegion
func (x *Model_TrainingRun_TrainingOptions) GetHorizon() int64
func (x *Model_TrainingRun_TrainingOptions) GetIncludeDrift() bool
func (x *Model_TrainingRun_TrainingOptions) GetInitialLearnRate() float64
func (x *Model_TrainingRun_TrainingOptions) GetInputLabelColumns() []string
func (x *Model_TrainingRun_TrainingOptions) GetItemColumn() string
func (x *Model_TrainingRun_TrainingOptions) GetKmeansInitializationColumn() string
func (x *Model_TrainingRun_TrainingOptions) GetKmeansInitializationMethod() Model_KmeansEnums_KmeansInitializationMethod
func (x *Model_TrainingRun_TrainingOptions) GetL1Regularization() *wrapperspb.DoubleValue
func (x *Model_TrainingRun_TrainingOptions) GetL2Regularization() *wrapperspb.DoubleValue
func (x *Model_TrainingRun_TrainingOptions) GetLabelClassWeights() map[string]float64
func (x *Model_TrainingRun_TrainingOptions) GetLearnRate() float64
func (x *Model_TrainingRun_TrainingOptions) GetLearnRateStrategy() Model_LearnRateStrategy
func (x *Model_TrainingRun_TrainingOptions) GetLossType() Model_LossType
func (x *Model_TrainingRun_TrainingOptions) GetMaxIterations() int64
func (x *Model_TrainingRun_TrainingOptions) GetMaxTreeDepth() int64
func (x *Model_TrainingRun_TrainingOptions) GetMinRelativeProgress() *wrapperspb.DoubleValue
func (x *Model_TrainingRun_TrainingOptions) GetMinSplitLoss() *wrapperspb.DoubleValue
func (x *Model_TrainingRun_TrainingOptions) GetModelUri() string
func (x *Model_TrainingRun_TrainingOptions) GetNonSeasonalOrder() *Model_ArimaOrder
func (x *Model_TrainingRun_TrainingOptions) GetNumClusters() int64
func (x *Model_TrainingRun_TrainingOptions) GetNumFactors() int64
func (x *Model_TrainingRun_TrainingOptions) GetOptimizationStrategy() Model_OptimizationStrategy
func (x *Model_TrainingRun_TrainingOptions) GetPreserveInputStructs() bool
func (x *Model_TrainingRun_TrainingOptions) GetSubsample() float64
func (x *Model_TrainingRun_TrainingOptions) GetTimeSeriesDataColumn() string
func (x *Model_TrainingRun_TrainingOptions) GetTimeSeriesIdColumn() string
func (x *Model_TrainingRun_TrainingOptions) GetTimeSeriesIdColumns() []string
func (x *Model_TrainingRun_TrainingOptions) GetTimeSeriesTimestampColumn() string
func (x *Model_TrainingRun_TrainingOptions) GetUserColumn() string
func (x *Model_TrainingRun_TrainingOptions) GetWalsAlpha() *wrapperspb.DoubleValue
func (x *Model_TrainingRun_TrainingOptions) GetWarmStart() *wrapperspb.BoolValue
func (*Model_TrainingRun_TrainingOptions) ProtoMessage()
func (x *Model_TrainingRun_TrainingOptions) ProtoReflect() protoreflect.Message
func (x *Model_TrainingRun_TrainingOptions) Reset()
func (x *Model_TrainingRun_TrainingOptions) String() string
type PatchModelRequest struct {
// Required. Project ID of the model to patch.
ProjectId string `protobuf:"bytes,1,opt,name=project_id,json=projectId,proto3" json:"project_id,omitempty"`
// Required. Dataset ID of the model to patch.
DatasetId string `protobuf:"bytes,2,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
// Required. Model ID of the model to patch.
ModelId string `protobuf:"bytes,3,opt,name=model_id,json=modelId,proto3" json:"model_id,omitempty"`
// Required. Patched model.
// Follows RFC5789 patch semantics. Missing fields are not updated.
// To clear a field, explicitly set to default value.
Model *Model `protobuf:"bytes,4,opt,name=model,proto3" json:"model,omitempty"`
// contains filtered or unexported fields
}
func (*PatchModelRequest) Descriptor() ([]byte, []int)
Deprecated: Use PatchModelRequest.ProtoReflect.Descriptor instead.
func (x *PatchModelRequest) GetDatasetId() string
func (x *PatchModelRequest) GetModel() *Model
func (x *PatchModelRequest) GetModelId() string
func (x *PatchModelRequest) GetProjectId() string
func (*PatchModelRequest) ProtoMessage()
func (x *PatchModelRequest) ProtoReflect() protoreflect.Message
func (x *PatchModelRequest) Reset()
func (x *PatchModelRequest) String() string
The type of a variable, e.g., a function argument. Examples: INT64: {type_kind="INT64"} ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"} STRUCT<x STRING, y ARRAY<DATE>>:
{type_kind="STRUCT",
struct_type={fields=[
{name="x", type={type_kind="STRING"}},
{name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
]}}
type StandardSqlDataType struct {
// Required. The top level type of this field.
// Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
TypeKind StandardSqlDataType_TypeKind `protobuf:"varint,1,opt,name=type_kind,json=typeKind,proto3,enum=google.cloud.bigquery.v2.StandardSqlDataType_TypeKind" json:"type_kind,omitempty"`
// Types that are assignable to SubType:
// *StandardSqlDataType_ArrayElementType
// *StandardSqlDataType_StructType
SubType isStandardSqlDataType_SubType `protobuf_oneof:"sub_type"`
// contains filtered or unexported fields
}
func (*StandardSqlDataType) Descriptor() ([]byte, []int)
Deprecated: Use StandardSqlDataType.ProtoReflect.Descriptor instead.
func (x *StandardSqlDataType) GetArrayElementType() *StandardSqlDataType
func (x *StandardSqlDataType) GetStructType() *StandardSqlStructType
func (m *StandardSqlDataType) GetSubType() isStandardSqlDataType_SubType
func (x *StandardSqlDataType) GetTypeKind() StandardSqlDataType_TypeKind
func (*StandardSqlDataType) ProtoMessage()
func (x *StandardSqlDataType) ProtoReflect() protoreflect.Message
func (x *StandardSqlDataType) Reset()
func (x *StandardSqlDataType) String() string
type StandardSqlDataType_ArrayElementType struct {
// The type of the array's elements, if type_kind = "ARRAY".
ArrayElementType *StandardSqlDataType `protobuf:"bytes,2,opt,name=array_element_type,json=arrayElementType,proto3,oneof"`
}
type StandardSqlDataType_StructType struct {
// The fields of this struct, in order, if type_kind = "STRUCT".
StructType *StandardSqlStructType `protobuf:"bytes,3,opt,name=struct_type,json=structType,proto3,oneof"`
}
type StandardSqlDataType_TypeKind int32
const (
// Invalid type.
StandardSqlDataType_TYPE_KIND_UNSPECIFIED StandardSqlDataType_TypeKind = 0
// Encoded as a string in decimal format.
StandardSqlDataType_INT64 StandardSqlDataType_TypeKind = 2
// Encoded as a boolean "false" or "true".
StandardSqlDataType_BOOL StandardSqlDataType_TypeKind = 5
// Encoded as a number, or string "NaN", "Infinity" or "-Infinity".
StandardSqlDataType_FLOAT64 StandardSqlDataType_TypeKind = 7
// Encoded as a string value.
StandardSqlDataType_STRING StandardSqlDataType_TypeKind = 8
// Encoded as a base64 string per RFC 4648, section 4.
StandardSqlDataType_BYTES StandardSqlDataType_TypeKind = 9
// Encoded as an RFC 3339 timestamp with mandatory "Z" time zone string:
// 1985-04-12T23:20:50.52Z
StandardSqlDataType_TIMESTAMP StandardSqlDataType_TypeKind = 19
// Encoded as RFC 3339 full-date format string: 1985-04-12
StandardSqlDataType_DATE StandardSqlDataType_TypeKind = 10
// Encoded as RFC 3339 partial-time format string: 23:20:50.52
StandardSqlDataType_TIME StandardSqlDataType_TypeKind = 20
// Encoded as RFC 3339 full-date "T" partial-time: 1985-04-12T23:20:50.52
StandardSqlDataType_DATETIME StandardSqlDataType_TypeKind = 21
// Encoded as fully qualified 3 part: 0-5 15 2:30:45.6
StandardSqlDataType_INTERVAL StandardSqlDataType_TypeKind = 26
// Encoded as WKT
StandardSqlDataType_GEOGRAPHY StandardSqlDataType_TypeKind = 22
// Encoded as a decimal string.
StandardSqlDataType_NUMERIC StandardSqlDataType_TypeKind = 23
// Encoded as a decimal string.
StandardSqlDataType_BIGNUMERIC StandardSqlDataType_TypeKind = 24
// Encoded as a string.
StandardSqlDataType_JSON StandardSqlDataType_TypeKind = 25
// Encoded as a list with types matching Type.array_type.
StandardSqlDataType_ARRAY StandardSqlDataType_TypeKind = 16
// Encoded as a list with fields of type Type.struct_type[i]. List is used
// because a JSON object cannot have duplicate field names.
StandardSqlDataType_STRUCT StandardSqlDataType_TypeKind = 17
)
func (StandardSqlDataType_TypeKind) Descriptor() protoreflect.EnumDescriptor
func (x StandardSqlDataType_TypeKind) Enum() *StandardSqlDataType_TypeKind
func (StandardSqlDataType_TypeKind) EnumDescriptor() ([]byte, []int)
Deprecated: Use StandardSqlDataType_TypeKind.Descriptor instead.
func (x StandardSqlDataType_TypeKind) Number() protoreflect.EnumNumber
func (x StandardSqlDataType_TypeKind) String() string
func (StandardSqlDataType_TypeKind) Type() protoreflect.EnumType
A field or a column.
type StandardSqlField struct {
// Optional. The name of this field. Can be absent for struct fields.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Optional. The type of this parameter. Absent if not explicitly
// specified (e.g., CREATE FUNCTION statement can omit the return type;
// in this case the output parameter does not have this "type" field).
Type *StandardSqlDataType `protobuf:"bytes,2,opt,name=type,proto3" json:"type,omitempty"`
// contains filtered or unexported fields
}
func (*StandardSqlField) Descriptor() ([]byte, []int)
Deprecated: Use StandardSqlField.ProtoReflect.Descriptor instead.
func (x *StandardSqlField) GetName() string
func (x *StandardSqlField) GetType() *StandardSqlDataType
func (*StandardSqlField) ProtoMessage()
func (x *StandardSqlField) ProtoReflect() protoreflect.Message
func (x *StandardSqlField) Reset()
func (x *StandardSqlField) String() string
type StandardSqlStructType struct {
Fields []*StandardSqlField `protobuf:"bytes,1,rep,name=fields,proto3" json:"fields,omitempty"`
// contains filtered or unexported fields
}
func (*StandardSqlStructType) Descriptor() ([]byte, []int)
Deprecated: Use StandardSqlStructType.ProtoReflect.Descriptor instead.
func (x *StandardSqlStructType) GetFields() []*StandardSqlField
func (*StandardSqlStructType) ProtoMessage()
func (x *StandardSqlStructType) ProtoReflect() protoreflect.Message
func (x *StandardSqlStructType) Reset()
func (x *StandardSqlStructType) String() string
A table type
type StandardSqlTableType struct {
// The columns in this table type
Columns []*StandardSqlField `protobuf:"bytes,1,rep,name=columns,proto3" json:"columns,omitempty"`
// contains filtered or unexported fields
}
func (*StandardSqlTableType) Descriptor() ([]byte, []int)
Deprecated: Use StandardSqlTableType.ProtoReflect.Descriptor instead.
func (x *StandardSqlTableType) GetColumns() []*StandardSqlField
func (*StandardSqlTableType) ProtoMessage()
func (x *StandardSqlTableType) ProtoReflect() protoreflect.Message
func (x *StandardSqlTableType) Reset()
func (x *StandardSqlTableType) String() string
type TableReference struct {
// Required. The ID of the project containing this table.
ProjectId string `protobuf:"bytes,1,opt,name=project_id,json=projectId,proto3" json:"project_id,omitempty"`
// Required. The ID of the dataset containing this table.
DatasetId string `protobuf:"bytes,2,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
// Required. The ID of the table. The ID must contain only
// letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum
// length is 1,024 characters. Certain operations allow
// suffixing of the table ID with a partition decorator, such as
// `sample_table$20190123`.
TableId string `protobuf:"bytes,3,opt,name=table_id,json=tableId,proto3" json:"table_id,omitempty"`
// The alternative field that will be used when ESF is not able to translate
// the received data to the project_id field.
ProjectIdAlternative []string `protobuf:"bytes,4,rep,name=project_id_alternative,json=projectIdAlternative,proto3" json:"project_id_alternative,omitempty"`
// The alternative field that will be used when ESF is not able to translate
// the received data to the project_id field.
DatasetIdAlternative []string `protobuf:"bytes,5,rep,name=dataset_id_alternative,json=datasetIdAlternative,proto3" json:"dataset_id_alternative,omitempty"`
// The alternative field that will be used when ESF is not able to translate
// the received data to the project_id field.
TableIdAlternative []string `protobuf:"bytes,6,rep,name=table_id_alternative,json=tableIdAlternative,proto3" json:"table_id_alternative,omitempty"`
// contains filtered or unexported fields
}
func (*TableReference) Descriptor() ([]byte, []int)
Deprecated: Use TableReference.ProtoReflect.Descriptor instead.
func (x *TableReference) GetDatasetId() string
func (x *TableReference) GetDatasetIdAlternative() []string
func (x *TableReference) GetProjectId() string
func (x *TableReference) GetProjectIdAlternative() []string
func (x *TableReference) GetTableId() string
func (x *TableReference) GetTableIdAlternative() []string
func (*TableReference) ProtoMessage()
func (x *TableReference) ProtoReflect() protoreflect.Message
func (x *TableReference) Reset()
func (x *TableReference) String() string
UnimplementedModelServiceServer can be embedded to have forward compatible implementations.
type UnimplementedModelServiceServer struct {
}
func (*UnimplementedModelServiceServer) DeleteModel(context.Context, *DeleteModelRequest) (*emptypb.Empty, error)
func (*UnimplementedModelServiceServer) GetModel(context.Context, *GetModelRequest) (*Model, error)
func (*UnimplementedModelServiceServer) ListModels(context.Context, *ListModelsRequest) (*ListModelsResponse, error)
func (*UnimplementedModelServiceServer) PatchModel(context.Context, *PatchModelRequest) (*Model, error)