| // Code generated by protoc-gen-go. DO NOT EDIT. |
| // source: google/cloud/bigquery/v2/model.proto |
| |
| package bigquery |
| |
| import ( |
| context "context" |
| fmt "fmt" |
| math "math" |
| |
| proto "github.com/golang/protobuf/proto" |
| empty "github.com/golang/protobuf/ptypes/empty" |
| timestamp "github.com/golang/protobuf/ptypes/timestamp" |
| wrappers "github.com/golang/protobuf/ptypes/wrappers" |
| _ "google.golang.org/genproto/googleapis/api/annotations" |
| grpc "google.golang.org/grpc" |
| codes "google.golang.org/grpc/codes" |
| status "google.golang.org/grpc/status" |
| ) |
| |
| // Reference imports to suppress errors if they are not otherwise used. |
| var _ = proto.Marshal |
| var _ = fmt.Errorf |
| var _ = math.Inf |
| |
| // This is a compile-time assertion to ensure that this generated file |
| // is compatible with the proto package it is being compiled against. |
| // A compilation error at this line likely means your copy of the |
| // proto package needs to be updated. |
| const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package |
| |
| // 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 |
| // [Beta] An imported TensorFlow model. |
| Model_TENSORFLOW Model_ModelType = 6 |
| ) |
| |
| var Model_ModelType_name = map[int32]string{ |
| 0: "MODEL_TYPE_UNSPECIFIED", |
| 1: "LINEAR_REGRESSION", |
| 2: "LOGISTIC_REGRESSION", |
| 3: "KMEANS", |
| 6: "TENSORFLOW", |
| } |
| |
| var Model_ModelType_value = map[string]int32{ |
| "MODEL_TYPE_UNSPECIFIED": 0, |
| "LINEAR_REGRESSION": 1, |
| "LOGISTIC_REGRESSION": 2, |
| "KMEANS": 3, |
| "TENSORFLOW": 6, |
| } |
| |
| func (x Model_ModelType) String() string { |
| return proto.EnumName(Model_ModelType_name, int32(x)) |
| } |
| |
| func (Model_ModelType) EnumDescriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 0} |
| } |
| |
| // 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 |
| ) |
| |
| var Model_LossType_name = map[int32]string{ |
| 0: "LOSS_TYPE_UNSPECIFIED", |
| 1: "MEAN_SQUARED_LOSS", |
| 2: "MEAN_LOG_LOSS", |
| } |
| |
| var Model_LossType_value = map[string]int32{ |
| "LOSS_TYPE_UNSPECIFIED": 0, |
| "MEAN_SQUARED_LOSS": 1, |
| "MEAN_LOG_LOSS": 2, |
| } |
| |
| func (x Model_LossType) String() string { |
| return proto.EnumName(Model_LossType_name, int32(x)) |
| } |
| |
| func (Model_LossType) EnumDescriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 1} |
| } |
| |
| // 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 |
| ) |
| |
| var Model_DistanceType_name = map[int32]string{ |
| 0: "DISTANCE_TYPE_UNSPECIFIED", |
| 1: "EUCLIDEAN", |
| 2: "COSINE", |
| } |
| |
| var Model_DistanceType_value = map[string]int32{ |
| "DISTANCE_TYPE_UNSPECIFIED": 0, |
| "EUCLIDEAN": 1, |
| "COSINE": 2, |
| } |
| |
| func (x Model_DistanceType) String() string { |
| return proto.EnumName(Model_DistanceType_name, int32(x)) |
| } |
| |
| func (Model_DistanceType) EnumDescriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 2} |
| } |
| |
| // 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 |
| ) |
| |
| var Model_DataSplitMethod_name = map[int32]string{ |
| 0: "DATA_SPLIT_METHOD_UNSPECIFIED", |
| 1: "RANDOM", |
| 2: "CUSTOM", |
| 3: "SEQUENTIAL", |
| 4: "NO_SPLIT", |
| 5: "AUTO_SPLIT", |
| } |
| |
| var Model_DataSplitMethod_value = map[string]int32{ |
| "DATA_SPLIT_METHOD_UNSPECIFIED": 0, |
| "RANDOM": 1, |
| "CUSTOM": 2, |
| "SEQUENTIAL": 3, |
| "NO_SPLIT": 4, |
| "AUTO_SPLIT": 5, |
| } |
| |
| func (x Model_DataSplitMethod) String() string { |
| return proto.EnumName(Model_DataSplitMethod_name, int32(x)) |
| } |
| |
| func (Model_DataSplitMethod) EnumDescriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 3} |
| } |
| |
| // 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 |
| ) |
| |
| var Model_LearnRateStrategy_name = map[int32]string{ |
| 0: "LEARN_RATE_STRATEGY_UNSPECIFIED", |
| 1: "LINE_SEARCH", |
| 2: "CONSTANT", |
| } |
| |
| var Model_LearnRateStrategy_value = map[string]int32{ |
| "LEARN_RATE_STRATEGY_UNSPECIFIED": 0, |
| "LINE_SEARCH": 1, |
| "CONSTANT": 2, |
| } |
| |
| func (x Model_LearnRateStrategy) String() string { |
| return proto.EnumName(Model_LearnRateStrategy_name, int32(x)) |
| } |
| |
| func (Model_LearnRateStrategy) EnumDescriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 4} |
| } |
| |
| // 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 |
| ) |
| |
| var Model_OptimizationStrategy_name = map[int32]string{ |
| 0: "OPTIMIZATION_STRATEGY_UNSPECIFIED", |
| 1: "BATCH_GRADIENT_DESCENT", |
| 2: "NORMAL_EQUATION", |
| } |
| |
| var Model_OptimizationStrategy_value = map[string]int32{ |
| "OPTIMIZATION_STRATEGY_UNSPECIFIED": 0, |
| "BATCH_GRADIENT_DESCENT": 1, |
| "NORMAL_EQUATION": 2, |
| } |
| |
| func (x Model_OptimizationStrategy) String() string { |
| return proto.EnumName(Model_OptimizationStrategy_name, int32(x)) |
| } |
| |
| func (Model_OptimizationStrategy) EnumDescriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 5} |
| } |
| |
| // Indicates the method used to initialize the centroids for KMeans |
| // clustering algorithm. |
| type Model_KmeansEnums_KmeansInitializationMethod int32 |
| |
| const ( |
| 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 |
| ) |
| |
| var Model_KmeansEnums_KmeansInitializationMethod_name = map[int32]string{ |
| 0: "KMEANS_INITIALIZATION_METHOD_UNSPECIFIED", |
| 1: "RANDOM", |
| 2: "CUSTOM", |
| } |
| |
| var Model_KmeansEnums_KmeansInitializationMethod_value = map[string]int32{ |
| "KMEANS_INITIALIZATION_METHOD_UNSPECIFIED": 0, |
| "RANDOM": 1, |
| "CUSTOM": 2, |
| } |
| |
| func (x Model_KmeansEnums_KmeansInitializationMethod) String() string { |
| return proto.EnumName(Model_KmeansEnums_KmeansInitializationMethod_name, int32(x)) |
| } |
| |
| func (Model_KmeansEnums_KmeansInitializationMethod) EnumDescriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 0, 0} |
| } |
| |
| 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. |
| 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model) Reset() { *m = Model{} } |
| func (m *Model) String() string { return proto.CompactTextString(m) } |
| func (*Model) ProtoMessage() {} |
| func (*Model) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0} |
| } |
| |
| func (m *Model) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model.Unmarshal(m, b) |
| } |
| func (m *Model) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model.Marshal(b, m, deterministic) |
| } |
| func (m *Model) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model.Merge(m, src) |
| } |
| func (m *Model) XXX_Size() int { |
| return xxx_messageInfo_Model.Size(m) |
| } |
| func (m *Model) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model proto.InternalMessageInfo |
| |
| func (m *Model) GetEtag() string { |
| if m != nil { |
| return m.Etag |
| } |
| return "" |
| } |
| |
| func (m *Model) GetModelReference() *ModelReference { |
| if m != nil { |
| return m.ModelReference |
| } |
| return nil |
| } |
| |
| func (m *Model) GetCreationTime() int64 { |
| if m != nil { |
| return m.CreationTime |
| } |
| return 0 |
| } |
| |
| func (m *Model) GetLastModifiedTime() int64 { |
| if m != nil { |
| return m.LastModifiedTime |
| } |
| return 0 |
| } |
| |
| func (m *Model) GetDescription() string { |
| if m != nil { |
| return m.Description |
| } |
| return "" |
| } |
| |
| func (m *Model) GetFriendlyName() string { |
| if m != nil { |
| return m.FriendlyName |
| } |
| return "" |
| } |
| |
| func (m *Model) GetLabels() map[string]string { |
| if m != nil { |
| return m.Labels |
| } |
| return nil |
| } |
| |
| func (m *Model) GetExpirationTime() int64 { |
| if m != nil { |
| return m.ExpirationTime |
| } |
| return 0 |
| } |
| |
| func (m *Model) GetLocation() string { |
| if m != nil { |
| return m.Location |
| } |
| return "" |
| } |
| |
| func (m *Model) GetEncryptionConfiguration() *EncryptionConfiguration { |
| if m != nil { |
| return m.EncryptionConfiguration |
| } |
| return nil |
| } |
| |
| func (m *Model) GetModelType() Model_ModelType { |
| if m != nil { |
| return m.ModelType |
| } |
| return Model_MODEL_TYPE_UNSPECIFIED |
| } |
| |
| func (m *Model) GetTrainingRuns() []*Model_TrainingRun { |
| if m != nil { |
| return m.TrainingRuns |
| } |
| return nil |
| } |
| |
| func (m *Model) GetFeatureColumns() []*StandardSqlField { |
| if m != nil { |
| return m.FeatureColumns |
| } |
| return nil |
| } |
| |
| func (m *Model) GetLabelColumns() []*StandardSqlField { |
| if m != nil { |
| return m.LabelColumns |
| } |
| return nil |
| } |
| |
| type Model_KmeansEnums struct { |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_KmeansEnums) Reset() { *m = Model_KmeansEnums{} } |
| func (m *Model_KmeansEnums) String() string { return proto.CompactTextString(m) } |
| func (*Model_KmeansEnums) ProtoMessage() {} |
| func (*Model_KmeansEnums) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 0} |
| } |
| |
| func (m *Model_KmeansEnums) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_KmeansEnums.Unmarshal(m, b) |
| } |
| func (m *Model_KmeansEnums) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_KmeansEnums.Marshal(b, m, deterministic) |
| } |
| func (m *Model_KmeansEnums) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_KmeansEnums.Merge(m, src) |
| } |
| func (m *Model_KmeansEnums) XXX_Size() int { |
| return xxx_messageInfo_Model_KmeansEnums.Size(m) |
| } |
| func (m *Model_KmeansEnums) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_KmeansEnums.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_KmeansEnums proto.InternalMessageInfo |
| |
| // Evaluation metrics for regression and explicit feedback type matrix |
| // factorization models. |
| type Model_RegressionMetrics struct { |
| // Mean absolute error. |
| MeanAbsoluteError *wrappers.DoubleValue `protobuf:"bytes,1,opt,name=mean_absolute_error,json=meanAbsoluteError,proto3" json:"mean_absolute_error,omitempty"` |
| // Mean squared error. |
| MeanSquaredError *wrappers.DoubleValue `protobuf:"bytes,2,opt,name=mean_squared_error,json=meanSquaredError,proto3" json:"mean_squared_error,omitempty"` |
| // Mean squared log error. |
| MeanSquaredLogError *wrappers.DoubleValue `protobuf:"bytes,3,opt,name=mean_squared_log_error,json=meanSquaredLogError,proto3" json:"mean_squared_log_error,omitempty"` |
| // Median absolute error. |
| MedianAbsoluteError *wrappers.DoubleValue `protobuf:"bytes,4,opt,name=median_absolute_error,json=medianAbsoluteError,proto3" json:"median_absolute_error,omitempty"` |
| // R^2 score. |
| RSquared *wrappers.DoubleValue `protobuf:"bytes,5,opt,name=r_squared,json=rSquared,proto3" json:"r_squared,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_RegressionMetrics) Reset() { *m = Model_RegressionMetrics{} } |
| func (m *Model_RegressionMetrics) String() string { return proto.CompactTextString(m) } |
| func (*Model_RegressionMetrics) ProtoMessage() {} |
| func (*Model_RegressionMetrics) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 1} |
| } |
| |
| func (m *Model_RegressionMetrics) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_RegressionMetrics.Unmarshal(m, b) |
| } |
| func (m *Model_RegressionMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_RegressionMetrics.Marshal(b, m, deterministic) |
| } |
| func (m *Model_RegressionMetrics) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_RegressionMetrics.Merge(m, src) |
| } |
| func (m *Model_RegressionMetrics) XXX_Size() int { |
| return xxx_messageInfo_Model_RegressionMetrics.Size(m) |
| } |
| func (m *Model_RegressionMetrics) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_RegressionMetrics.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_RegressionMetrics proto.InternalMessageInfo |
| |
| func (m *Model_RegressionMetrics) GetMeanAbsoluteError() *wrappers.DoubleValue { |
| if m != nil { |
| return m.MeanAbsoluteError |
| } |
| return nil |
| } |
| |
| func (m *Model_RegressionMetrics) GetMeanSquaredError() *wrappers.DoubleValue { |
| if m != nil { |
| return m.MeanSquaredError |
| } |
| return nil |
| } |
| |
| func (m *Model_RegressionMetrics) GetMeanSquaredLogError() *wrappers.DoubleValue { |
| if m != nil { |
| return m.MeanSquaredLogError |
| } |
| return nil |
| } |
| |
| func (m *Model_RegressionMetrics) GetMedianAbsoluteError() *wrappers.DoubleValue { |
| if m != nil { |
| return m.MedianAbsoluteError |
| } |
| return nil |
| } |
| |
| func (m *Model_RegressionMetrics) GetRSquared() *wrappers.DoubleValue { |
| if m != nil { |
| return m.RSquared |
| } |
| return nil |
| } |
| |
| // 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 *wrappers.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 *wrappers.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 *wrappers.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 *wrappers.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 *wrappers.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 *wrappers.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 *wrappers.DoubleValue `protobuf:"bytes,7,opt,name=roc_auc,json=rocAuc,proto3" json:"roc_auc,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_AggregateClassificationMetrics) Reset() { *m = Model_AggregateClassificationMetrics{} } |
| func (m *Model_AggregateClassificationMetrics) String() string { return proto.CompactTextString(m) } |
| func (*Model_AggregateClassificationMetrics) ProtoMessage() {} |
| func (*Model_AggregateClassificationMetrics) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 2} |
| } |
| |
| func (m *Model_AggregateClassificationMetrics) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_AggregateClassificationMetrics.Unmarshal(m, b) |
| } |
| func (m *Model_AggregateClassificationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_AggregateClassificationMetrics.Marshal(b, m, deterministic) |
| } |
| func (m *Model_AggregateClassificationMetrics) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_AggregateClassificationMetrics.Merge(m, src) |
| } |
| func (m *Model_AggregateClassificationMetrics) XXX_Size() int { |
| return xxx_messageInfo_Model_AggregateClassificationMetrics.Size(m) |
| } |
| func (m *Model_AggregateClassificationMetrics) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_AggregateClassificationMetrics.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_AggregateClassificationMetrics proto.InternalMessageInfo |
| |
| func (m *Model_AggregateClassificationMetrics) GetPrecision() *wrappers.DoubleValue { |
| if m != nil { |
| return m.Precision |
| } |
| return nil |
| } |
| |
| func (m *Model_AggregateClassificationMetrics) GetRecall() *wrappers.DoubleValue { |
| if m != nil { |
| return m.Recall |
| } |
| return nil |
| } |
| |
| func (m *Model_AggregateClassificationMetrics) GetAccuracy() *wrappers.DoubleValue { |
| if m != nil { |
| return m.Accuracy |
| } |
| return nil |
| } |
| |
| func (m *Model_AggregateClassificationMetrics) GetThreshold() *wrappers.DoubleValue { |
| if m != nil { |
| return m.Threshold |
| } |
| return nil |
| } |
| |
| func (m *Model_AggregateClassificationMetrics) GetF1Score() *wrappers.DoubleValue { |
| if m != nil { |
| return m.F1Score |
| } |
| return nil |
| } |
| |
| func (m *Model_AggregateClassificationMetrics) GetLogLoss() *wrappers.DoubleValue { |
| if m != nil { |
| return m.LogLoss |
| } |
| return nil |
| } |
| |
| func (m *Model_AggregateClassificationMetrics) GetRocAuc() *wrappers.DoubleValue { |
| if m != nil { |
| return m.RocAuc |
| } |
| return nil |
| } |
| |
| // 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_BinaryClassificationMetrics) Reset() { *m = Model_BinaryClassificationMetrics{} } |
| func (m *Model_BinaryClassificationMetrics) String() string { return proto.CompactTextString(m) } |
| func (*Model_BinaryClassificationMetrics) ProtoMessage() {} |
| func (*Model_BinaryClassificationMetrics) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 3} |
| } |
| |
| func (m *Model_BinaryClassificationMetrics) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_BinaryClassificationMetrics.Unmarshal(m, b) |
| } |
| func (m *Model_BinaryClassificationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_BinaryClassificationMetrics.Marshal(b, m, deterministic) |
| } |
| func (m *Model_BinaryClassificationMetrics) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_BinaryClassificationMetrics.Merge(m, src) |
| } |
| func (m *Model_BinaryClassificationMetrics) XXX_Size() int { |
| return xxx_messageInfo_Model_BinaryClassificationMetrics.Size(m) |
| } |
| func (m *Model_BinaryClassificationMetrics) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_BinaryClassificationMetrics.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_BinaryClassificationMetrics proto.InternalMessageInfo |
| |
| func (m *Model_BinaryClassificationMetrics) GetAggregateClassificationMetrics() *Model_AggregateClassificationMetrics { |
| if m != nil { |
| return m.AggregateClassificationMetrics |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics) GetBinaryConfusionMatrixList() []*Model_BinaryClassificationMetrics_BinaryConfusionMatrix { |
| if m != nil { |
| return m.BinaryConfusionMatrixList |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics) GetPositiveLabel() string { |
| if m != nil { |
| return m.PositiveLabel |
| } |
| return "" |
| } |
| |
| func (m *Model_BinaryClassificationMetrics) GetNegativeLabel() string { |
| if m != nil { |
| return m.NegativeLabel |
| } |
| return "" |
| } |
| |
| // Confusion matrix for binary classification models. |
| type Model_BinaryClassificationMetrics_BinaryConfusionMatrix struct { |
| // Threshold value used when computing each of the following metric. |
| PositiveClassThreshold *wrappers.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 *wrappers.Int64Value `protobuf:"bytes,2,opt,name=true_positives,json=truePositives,proto3" json:"true_positives,omitempty"` |
| // Number of false samples predicted as true. |
| FalsePositives *wrappers.Int64Value `protobuf:"bytes,3,opt,name=false_positives,json=falsePositives,proto3" json:"false_positives,omitempty"` |
| // Number of true samples predicted as false. |
| TrueNegatives *wrappers.Int64Value `protobuf:"bytes,4,opt,name=true_negatives,json=trueNegatives,proto3" json:"true_negatives,omitempty"` |
| // Number of false samples predicted as false. |
| FalseNegatives *wrappers.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 *wrappers.DoubleValue `protobuf:"bytes,6,opt,name=precision,proto3" json:"precision,omitempty"` |
| // The fraction of actual positive labels that were given a positive |
| // prediction. |
| Recall *wrappers.DoubleValue `protobuf:"bytes,7,opt,name=recall,proto3" json:"recall,omitempty"` |
| // The equally weighted average of recall and precision. |
| F1Score *wrappers.DoubleValue `protobuf:"bytes,8,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"` |
| // The fraction of predictions given the correct label. |
| Accuracy *wrappers.DoubleValue `protobuf:"bytes,9,opt,name=accuracy,proto3" json:"accuracy,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) Reset() { |
| *m = Model_BinaryClassificationMetrics_BinaryConfusionMatrix{} |
| } |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*Model_BinaryClassificationMetrics_BinaryConfusionMatrix) ProtoMessage() {} |
| func (*Model_BinaryClassificationMetrics_BinaryConfusionMatrix) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 3, 0} |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_BinaryClassificationMetrics_BinaryConfusionMatrix.Unmarshal(m, b) |
| } |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_BinaryClassificationMetrics_BinaryConfusionMatrix.Marshal(b, m, deterministic) |
| } |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_BinaryClassificationMetrics_BinaryConfusionMatrix.Merge(m, src) |
| } |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) XXX_Size() int { |
| return xxx_messageInfo_Model_BinaryClassificationMetrics_BinaryConfusionMatrix.Size(m) |
| } |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_BinaryClassificationMetrics_BinaryConfusionMatrix.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_BinaryClassificationMetrics_BinaryConfusionMatrix proto.InternalMessageInfo |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetPositiveClassThreshold() *wrappers.DoubleValue { |
| if m != nil { |
| return m.PositiveClassThreshold |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetTruePositives() *wrappers.Int64Value { |
| if m != nil { |
| return m.TruePositives |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetFalsePositives() *wrappers.Int64Value { |
| if m != nil { |
| return m.FalsePositives |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetTrueNegatives() *wrappers.Int64Value { |
| if m != nil { |
| return m.TrueNegatives |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetFalseNegatives() *wrappers.Int64Value { |
| if m != nil { |
| return m.FalseNegatives |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetPrecision() *wrappers.DoubleValue { |
| if m != nil { |
| return m.Precision |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetRecall() *wrappers.DoubleValue { |
| if m != nil { |
| return m.Recall |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetF1Score() *wrappers.DoubleValue { |
| if m != nil { |
| return m.F1Score |
| } |
| return nil |
| } |
| |
| func (m *Model_BinaryClassificationMetrics_BinaryConfusionMatrix) GetAccuracy() *wrappers.DoubleValue { |
| if m != nil { |
| return m.Accuracy |
| } |
| return nil |
| } |
| |
| // 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics) Reset() { *m = Model_MultiClassClassificationMetrics{} } |
| func (m *Model_MultiClassClassificationMetrics) String() string { return proto.CompactTextString(m) } |
| func (*Model_MultiClassClassificationMetrics) ProtoMessage() {} |
| func (*Model_MultiClassClassificationMetrics) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 4} |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics.Unmarshal(m, b) |
| } |
| func (m *Model_MultiClassClassificationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics.Marshal(b, m, deterministic) |
| } |
| func (m *Model_MultiClassClassificationMetrics) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_MultiClassClassificationMetrics.Merge(m, src) |
| } |
| func (m *Model_MultiClassClassificationMetrics) XXX_Size() int { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics.Size(m) |
| } |
| func (m *Model_MultiClassClassificationMetrics) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_MultiClassClassificationMetrics.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_MultiClassClassificationMetrics proto.InternalMessageInfo |
| |
| func (m *Model_MultiClassClassificationMetrics) GetAggregateClassificationMetrics() *Model_AggregateClassificationMetrics { |
| if m != nil { |
| return m.AggregateClassificationMetrics |
| } |
| return nil |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics) GetConfusionMatrixList() []*Model_MultiClassClassificationMetrics_ConfusionMatrix { |
| if m != nil { |
| return m.ConfusionMatrixList |
| } |
| return nil |
| } |
| |
| // Confusion matrix for multi-class classification models. |
| type Model_MultiClassClassificationMetrics_ConfusionMatrix struct { |
| // Confidence threshold used when computing the entries of the |
| // confusion matrix. |
| ConfidenceThreshold *wrappers.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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) Reset() { |
| *m = Model_MultiClassClassificationMetrics_ConfusionMatrix{} |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*Model_MultiClassClassificationMetrics_ConfusionMatrix) ProtoMessage() {} |
| func (*Model_MultiClassClassificationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 4, 0} |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix.Unmarshal(m, b) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix.Marshal(b, m, deterministic) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix.Merge(m, src) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) XXX_Size() int { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix.Size(m) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix proto.InternalMessageInfo |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) GetConfidenceThreshold() *wrappers.DoubleValue { |
| if m != nil { |
| return m.ConfidenceThreshold |
| } |
| return nil |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix) GetRows() []*Model_MultiClassClassificationMetrics_ConfusionMatrix_Row { |
| if m != nil { |
| return m.Rows |
| } |
| return nil |
| } |
| |
| // 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 *wrappers.Int64Value `protobuf:"bytes,2,opt,name=item_count,json=itemCount,proto3" json:"item_count,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) Reset() { |
| *m = Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry{} |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) ProtoMessage() {} |
| func (*Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 4, 0, 0} |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry.Unmarshal(m, b) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry.Marshal(b, m, deterministic) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry.Merge(m, src) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) XXX_Size() int { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry.Size(m) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry proto.InternalMessageInfo |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) GetPredictedLabel() string { |
| if m != nil { |
| return m.PredictedLabel |
| } |
| return "" |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry) GetItemCount() *wrappers.Int64Value { |
| if m != nil { |
| return m.ItemCount |
| } |
| return nil |
| } |
| |
| // 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) Reset() { |
| *m = Model_MultiClassClassificationMetrics_ConfusionMatrix_Row{} |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) ProtoMessage() {} |
| func (*Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 4, 0, 1} |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Row.Unmarshal(m, b) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Row.Marshal(b, m, deterministic) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Row.Merge(m, src) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) XXX_Size() int { |
| return xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Row.Size(m) |
| } |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Row.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_MultiClassClassificationMetrics_ConfusionMatrix_Row proto.InternalMessageInfo |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) GetActualLabel() string { |
| if m != nil { |
| return m.ActualLabel |
| } |
| return "" |
| } |
| |
| func (m *Model_MultiClassClassificationMetrics_ConfusionMatrix_Row) GetEntries() []*Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry { |
| if m != nil { |
| return m.Entries |
| } |
| return nil |
| } |
| |
| // Evaluation metrics for clustering models. |
| type Model_ClusteringMetrics struct { |
| // Davies-Bouldin index. |
| DaviesBouldinIndex *wrappers.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 *wrappers.DoubleValue `protobuf:"bytes,2,opt,name=mean_squared_distance,json=meanSquaredDistance,proto3" json:"mean_squared_distance,omitempty"` |
| // [Beta] Information for all clusters. |
| Clusters []*Model_ClusteringMetrics_Cluster `protobuf:"bytes,3,rep,name=clusters,proto3" json:"clusters,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_ClusteringMetrics) Reset() { *m = Model_ClusteringMetrics{} } |
| func (m *Model_ClusteringMetrics) String() string { return proto.CompactTextString(m) } |
| func (*Model_ClusteringMetrics) ProtoMessage() {} |
| func (*Model_ClusteringMetrics) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 5} |
| } |
| |
| func (m *Model_ClusteringMetrics) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_ClusteringMetrics.Unmarshal(m, b) |
| } |
| func (m *Model_ClusteringMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_ClusteringMetrics.Marshal(b, m, deterministic) |
| } |
| func (m *Model_ClusteringMetrics) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_ClusteringMetrics.Merge(m, src) |
| } |
| func (m *Model_ClusteringMetrics) XXX_Size() int { |
| return xxx_messageInfo_Model_ClusteringMetrics.Size(m) |
| } |
| func (m *Model_ClusteringMetrics) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_ClusteringMetrics.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_ClusteringMetrics proto.InternalMessageInfo |
| |
| func (m *Model_ClusteringMetrics) GetDaviesBouldinIndex() *wrappers.DoubleValue { |
| if m != nil { |
| return m.DaviesBouldinIndex |
| } |
| return nil |
| } |
| |
| func (m *Model_ClusteringMetrics) GetMeanSquaredDistance() *wrappers.DoubleValue { |
| if m != nil { |
| return m.MeanSquaredDistance |
| } |
| return nil |
| } |
| |
| func (m *Model_ClusteringMetrics) GetClusters() []*Model_ClusteringMetrics_Cluster { |
| if m != nil { |
| return m.Clusters |
| } |
| return nil |
| } |
| |
| // 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 *wrappers.Int64Value `protobuf:"bytes,3,opt,name=count,proto3" json:"count,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster) Reset() { *m = Model_ClusteringMetrics_Cluster{} } |
| func (m *Model_ClusteringMetrics_Cluster) String() string { return proto.CompactTextString(m) } |
| func (*Model_ClusteringMetrics_Cluster) ProtoMessage() {} |
| func (*Model_ClusteringMetrics_Cluster) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 5, 0} |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster.Unmarshal(m, b) |
| } |
| func (m *Model_ClusteringMetrics_Cluster) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster.Marshal(b, m, deterministic) |
| } |
| func (m *Model_ClusteringMetrics_Cluster) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_ClusteringMetrics_Cluster.Merge(m, src) |
| } |
| func (m *Model_ClusteringMetrics_Cluster) XXX_Size() int { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster.Size(m) |
| } |
| func (m *Model_ClusteringMetrics_Cluster) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_ClusteringMetrics_Cluster.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_ClusteringMetrics_Cluster proto.InternalMessageInfo |
| |
| func (m *Model_ClusteringMetrics_Cluster) GetCentroidId() int64 { |
| if m != nil { |
| return m.CentroidId |
| } |
| return 0 |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster) GetFeatureValues() []*Model_ClusteringMetrics_Cluster_FeatureValue { |
| if m != nil { |
| return m.FeatureValues |
| } |
| return nil |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster) GetCount() *wrappers.Int64Value { |
| if m != nil { |
| return m.Count |
| } |
| return nil |
| } |
| |
| // 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 valid to be assigned to Value: |
| // *Model_ClusteringMetrics_Cluster_FeatureValue_NumericalValue |
| // *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_ |
| Value isModel_ClusteringMetrics_Cluster_FeatureValue_Value `protobuf_oneof:"value"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) Reset() { |
| *m = Model_ClusteringMetrics_Cluster_FeatureValue{} |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue) ProtoMessage() {} |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 5, 0, 0} |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue.Unmarshal(m, b) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue.Marshal(b, m, deterministic) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue.Merge(m, src) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) XXX_Size() int { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue.Size(m) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue proto.InternalMessageInfo |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) GetFeatureColumn() string { |
| if m != nil { |
| return m.FeatureColumn |
| } |
| return "" |
| } |
| |
| type isModel_ClusteringMetrics_Cluster_FeatureValue_Value interface { |
| isModel_ClusteringMetrics_Cluster_FeatureValue_Value() |
| } |
| |
| type Model_ClusteringMetrics_Cluster_FeatureValue_NumericalValue struct { |
| NumericalValue *wrappers.DoubleValue `protobuf:"bytes,2,opt,name=numerical_value,json=numericalValue,proto3,oneof"` |
| } |
| |
| type Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_ struct { |
| CategoricalValue *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue `protobuf:"bytes,3,opt,name=categorical_value,json=categoricalValue,proto3,oneof"` |
| } |
| |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue_NumericalValue) isModel_ClusteringMetrics_Cluster_FeatureValue_Value() { |
| } |
| |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_) isModel_ClusteringMetrics_Cluster_FeatureValue_Value() { |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) GetValue() isModel_ClusteringMetrics_Cluster_FeatureValue_Value { |
| if m != nil { |
| return m.Value |
| } |
| return nil |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) GetNumericalValue() *wrappers.DoubleValue { |
| if x, ok := m.GetValue().(*Model_ClusteringMetrics_Cluster_FeatureValue_NumericalValue); ok { |
| return x.NumericalValue |
| } |
| return nil |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue) GetCategoricalValue() *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue { |
| if x, ok := m.GetValue().(*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_); ok { |
| return x.CategoricalValue |
| } |
| return nil |
| } |
| |
| // XXX_OneofWrappers is for the internal use of the proto package. |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue) XXX_OneofWrappers() []interface{} { |
| return []interface{}{ |
| (*Model_ClusteringMetrics_Cluster_FeatureValue_NumericalValue)(nil), |
| (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_)(nil), |
| } |
| } |
| |
| // 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) Reset() { |
| *m = Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue{} |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) ProtoMessage() {} |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 5, 0, 0, 0} |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue.Unmarshal(m, b) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue.Marshal(b, m, deterministic) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue.Merge(m, src) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) XXX_Size() int { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue.Size(m) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue proto.InternalMessageInfo |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue) GetCategoryCounts() []*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount { |
| if m != nil { |
| return m.CategoryCounts |
| } |
| return nil |
| } |
| |
| // 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 *wrappers.Int64Value `protobuf:"bytes,2,opt,name=count,proto3" json:"count,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) Reset() { |
| *m = Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount{} |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) ProtoMessage() {} |
| func (*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 5, 0, 0, 0, 0} |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount.Unmarshal(m, b) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount.Marshal(b, m, deterministic) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount.Merge(m, src) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) XXX_Size() int { |
| return xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount.Size(m) |
| } |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount proto.InternalMessageInfo |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) GetCategory() string { |
| if m != nil { |
| return m.Category |
| } |
| return "" |
| } |
| |
| func (m *Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount) GetCount() *wrappers.Int64Value { |
| if m != nil { |
| return m.Count |
| } |
| return nil |
| } |
| |
| // 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 valid to be assigned to Metrics: |
| // *Model_EvaluationMetrics_RegressionMetrics |
| // *Model_EvaluationMetrics_BinaryClassificationMetrics |
| // *Model_EvaluationMetrics_MultiClassClassificationMetrics |
| // *Model_EvaluationMetrics_ClusteringMetrics |
| Metrics isModel_EvaluationMetrics_Metrics `protobuf_oneof:"metrics"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_EvaluationMetrics) Reset() { *m = Model_EvaluationMetrics{} } |
| func (m *Model_EvaluationMetrics) String() string { return proto.CompactTextString(m) } |
| func (*Model_EvaluationMetrics) ProtoMessage() {} |
| func (*Model_EvaluationMetrics) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 6} |
| } |
| |
| func (m *Model_EvaluationMetrics) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_EvaluationMetrics.Unmarshal(m, b) |
| } |
| func (m *Model_EvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_EvaluationMetrics.Marshal(b, m, deterministic) |
| } |
| func (m *Model_EvaluationMetrics) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_EvaluationMetrics.Merge(m, src) |
| } |
| func (m *Model_EvaluationMetrics) XXX_Size() int { |
| return xxx_messageInfo_Model_EvaluationMetrics.Size(m) |
| } |
| func (m *Model_EvaluationMetrics) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_EvaluationMetrics.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_EvaluationMetrics proto.InternalMessageInfo |
| |
| type isModel_EvaluationMetrics_Metrics interface { |
| isModel_EvaluationMetrics_Metrics() |
| } |
| |
| type Model_EvaluationMetrics_RegressionMetrics struct { |
| RegressionMetrics *Model_RegressionMetrics `protobuf:"bytes,1,opt,name=regression_metrics,json=regressionMetrics,proto3,oneof"` |
| } |
| |
| type Model_EvaluationMetrics_BinaryClassificationMetrics struct { |
| BinaryClassificationMetrics *Model_BinaryClassificationMetrics `protobuf:"bytes,2,opt,name=binary_classification_metrics,json=binaryClassificationMetrics,proto3,oneof"` |
| } |
| |
| type Model_EvaluationMetrics_MultiClassClassificationMetrics struct { |
| MultiClassClassificationMetrics *Model_MultiClassClassificationMetrics `protobuf:"bytes,3,opt,name=multi_class_classification_metrics,json=multiClassClassificationMetrics,proto3,oneof"` |
| } |
| |
| type Model_EvaluationMetrics_ClusteringMetrics struct { |
| ClusteringMetrics *Model_ClusteringMetrics `protobuf:"bytes,4,opt,name=clustering_metrics,json=clusteringMetrics,proto3,oneof"` |
| } |
| |
| func (*Model_EvaluationMetrics_RegressionMetrics) isModel_EvaluationMetrics_Metrics() {} |
| |
| func (*Model_EvaluationMetrics_BinaryClassificationMetrics) isModel_EvaluationMetrics_Metrics() {} |
| |
| func (*Model_EvaluationMetrics_MultiClassClassificationMetrics) isModel_EvaluationMetrics_Metrics() {} |
| |
| func (*Model_EvaluationMetrics_ClusteringMetrics) isModel_EvaluationMetrics_Metrics() {} |
| |
| func (m *Model_EvaluationMetrics) GetMetrics() isModel_EvaluationMetrics_Metrics { |
| if m != nil { |
| return m.Metrics |
| } |
| return nil |
| } |
| |
| func (m *Model_EvaluationMetrics) GetRegressionMetrics() *Model_RegressionMetrics { |
| if x, ok := m.GetMetrics().(*Model_EvaluationMetrics_RegressionMetrics); ok { |
| return x.RegressionMetrics |
| } |
| return nil |
| } |
| |
| func (m *Model_EvaluationMetrics) GetBinaryClassificationMetrics() *Model_BinaryClassificationMetrics { |
| if x, ok := m.GetMetrics().(*Model_EvaluationMetrics_BinaryClassificationMetrics); ok { |
| return x.BinaryClassificationMetrics |
| } |
| return nil |
| } |
| |
| func (m *Model_EvaluationMetrics) GetMultiClassClassificationMetrics() *Model_MultiClassClassificationMetrics { |
| if x, ok := m.GetMetrics().(*Model_EvaluationMetrics_MultiClassClassificationMetrics); ok { |
| return x.MultiClassClassificationMetrics |
| } |
| return nil |
| } |
| |
| func (m *Model_EvaluationMetrics) GetClusteringMetrics() *Model_ClusteringMetrics { |
| if x, ok := m.GetMetrics().(*Model_EvaluationMetrics_ClusteringMetrics); ok { |
| return x.ClusteringMetrics |
| } |
| return nil |
| } |
| |
| // XXX_OneofWrappers is for the internal use of the proto package. |
| func (*Model_EvaluationMetrics) XXX_OneofWrappers() []interface{} { |
| return []interface{}{ |
| (*Model_EvaluationMetrics_RegressionMetrics)(nil), |
| (*Model_EvaluationMetrics_BinaryClassificationMetrics)(nil), |
| (*Model_EvaluationMetrics_MultiClassClassificationMetrics)(nil), |
| (*Model_EvaluationMetrics_ClusteringMetrics)(nil), |
| } |
| } |
| |
| // 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 *timestamp.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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_TrainingRun) Reset() { *m = Model_TrainingRun{} } |
| func (m *Model_TrainingRun) String() string { return proto.CompactTextString(m) } |
| func (*Model_TrainingRun) ProtoMessage() {} |
| func (*Model_TrainingRun) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 7} |
| } |
| |
| func (m *Model_TrainingRun) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_TrainingRun.Unmarshal(m, b) |
| } |
| func (m *Model_TrainingRun) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_TrainingRun.Marshal(b, m, deterministic) |
| } |
| func (m *Model_TrainingRun) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_TrainingRun.Merge(m, src) |
| } |
| func (m *Model_TrainingRun) XXX_Size() int { |
| return xxx_messageInfo_Model_TrainingRun.Size(m) |
| } |
| func (m *Model_TrainingRun) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_TrainingRun.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_TrainingRun proto.InternalMessageInfo |
| |
| func (m *Model_TrainingRun) GetTrainingOptions() *Model_TrainingRun_TrainingOptions { |
| if m != nil { |
| return m.TrainingOptions |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun) GetStartTime() *timestamp.Timestamp { |
| if m != nil { |
| return m.StartTime |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun) GetResults() []*Model_TrainingRun_IterationResult { |
| if m != nil { |
| return m.Results |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun) GetEvaluationMetrics() *Model_EvaluationMetrics { |
| if m != nil { |
| return m.EvaluationMetrics |
| } |
| return nil |
| } |
| |
| 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 *wrappers.DoubleValue `protobuf:"bytes,4,opt,name=l1_regularization,json=l1Regularization,proto3" json:"l1_regularization,omitempty"` |
| // L2 regularization coefficient. |
| L2Regularization *wrappers.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 *wrappers.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 *wrappers.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 *wrappers.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"` |
| // 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"` |
| // [Beta] 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"` |
| // 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) Reset() { *m = Model_TrainingRun_TrainingOptions{} } |
| func (m *Model_TrainingRun_TrainingOptions) String() string { return proto.CompactTextString(m) } |
| func (*Model_TrainingRun_TrainingOptions) ProtoMessage() {} |
| func (*Model_TrainingRun_TrainingOptions) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 7, 0} |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_TrainingRun_TrainingOptions.Unmarshal(m, b) |
| } |
| func (m *Model_TrainingRun_TrainingOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_TrainingRun_TrainingOptions.Marshal(b, m, deterministic) |
| } |
| func (m *Model_TrainingRun_TrainingOptions) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_TrainingRun_TrainingOptions.Merge(m, src) |
| } |
| func (m *Model_TrainingRun_TrainingOptions) XXX_Size() int { |
| return xxx_messageInfo_Model_TrainingRun_TrainingOptions.Size(m) |
| } |
| func (m *Model_TrainingRun_TrainingOptions) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_TrainingRun_TrainingOptions.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_TrainingRun_TrainingOptions proto.InternalMessageInfo |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetMaxIterations() int64 { |
| if m != nil { |
| return m.MaxIterations |
| } |
| return 0 |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetLossType() Model_LossType { |
| if m != nil { |
| return m.LossType |
| } |
| return Model_LOSS_TYPE_UNSPECIFIED |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetLearnRate() float64 { |
| if m != nil { |
| return m.LearnRate |
| } |
| return 0 |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetL1Regularization() *wrappers.DoubleValue { |
| if m != nil { |
| return m.L1Regularization |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetL2Regularization() *wrappers.DoubleValue { |
| if m != nil { |
| return m.L2Regularization |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetMinRelativeProgress() *wrappers.DoubleValue { |
| if m != nil { |
| return m.MinRelativeProgress |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetWarmStart() *wrappers.BoolValue { |
| if m != nil { |
| return m.WarmStart |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetEarlyStop() *wrappers.BoolValue { |
| if m != nil { |
| return m.EarlyStop |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetInputLabelColumns() []string { |
| if m != nil { |
| return m.InputLabelColumns |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetDataSplitMethod() Model_DataSplitMethod { |
| if m != nil { |
| return m.DataSplitMethod |
| } |
| return Model_DATA_SPLIT_METHOD_UNSPECIFIED |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetDataSplitEvalFraction() float64 { |
| if m != nil { |
| return m.DataSplitEvalFraction |
| } |
| return 0 |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetDataSplitColumn() string { |
| if m != nil { |
| return m.DataSplitColumn |
| } |
| return "" |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetLearnRateStrategy() Model_LearnRateStrategy { |
| if m != nil { |
| return m.LearnRateStrategy |
| } |
| return Model_LEARN_RATE_STRATEGY_UNSPECIFIED |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetInitialLearnRate() float64 { |
| if m != nil { |
| return m.InitialLearnRate |
| } |
| return 0 |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetLabelClassWeights() map[string]float64 { |
| if m != nil { |
| return m.LabelClassWeights |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetDistanceType() Model_DistanceType { |
| if m != nil { |
| return m.DistanceType |
| } |
| return Model_DISTANCE_TYPE_UNSPECIFIED |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetNumClusters() int64 { |
| if m != nil { |
| return m.NumClusters |
| } |
| return 0 |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetModelUri() string { |
| if m != nil { |
| return m.ModelUri |
| } |
| return "" |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetOptimizationStrategy() Model_OptimizationStrategy { |
| if m != nil { |
| return m.OptimizationStrategy |
| } |
| return Model_OPTIMIZATION_STRATEGY_UNSPECIFIED |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetKmeansInitializationMethod() Model_KmeansEnums_KmeansInitializationMethod { |
| if m != nil { |
| return m.KmeansInitializationMethod |
| } |
| return Model_KmeansEnums_KMEANS_INITIALIZATION_METHOD_UNSPECIFIED |
| } |
| |
| func (m *Model_TrainingRun_TrainingOptions) GetKmeansInitializationColumn() string { |
| if m != nil { |
| return m.KmeansInitializationColumn |
| } |
| return "" |
| } |
| |
| // Information about a single iteration of the training run. |
| type Model_TrainingRun_IterationResult struct { |
| // Index of the iteration, 0 based. |
| Index *wrappers.Int32Value `protobuf:"bytes,1,opt,name=index,proto3" json:"index,omitempty"` |
| // Time taken to run the iteration in milliseconds. |
| DurationMs *wrappers.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 *wrappers.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 *wrappers.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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_TrainingRun_IterationResult) Reset() { *m = Model_TrainingRun_IterationResult{} } |
| func (m *Model_TrainingRun_IterationResult) String() string { return proto.CompactTextString(m) } |
| func (*Model_TrainingRun_IterationResult) ProtoMessage() {} |
| func (*Model_TrainingRun_IterationResult) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 7, 1} |
| } |
| |
| func (m *Model_TrainingRun_IterationResult) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_TrainingRun_IterationResult.Unmarshal(m, b) |
| } |
| func (m *Model_TrainingRun_IterationResult) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_TrainingRun_IterationResult.Marshal(b, m, deterministic) |
| } |
| func (m *Model_TrainingRun_IterationResult) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_TrainingRun_IterationResult.Merge(m, src) |
| } |
| func (m *Model_TrainingRun_IterationResult) XXX_Size() int { |
| return xxx_messageInfo_Model_TrainingRun_IterationResult.Size(m) |
| } |
| func (m *Model_TrainingRun_IterationResult) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_TrainingRun_IterationResult.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_TrainingRun_IterationResult proto.InternalMessageInfo |
| |
| func (m *Model_TrainingRun_IterationResult) GetIndex() *wrappers.Int32Value { |
| if m != nil { |
| return m.Index |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_IterationResult) GetDurationMs() *wrappers.Int64Value { |
| if m != nil { |
| return m.DurationMs |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_IterationResult) GetTrainingLoss() *wrappers.DoubleValue { |
| if m != nil { |
| return m.TrainingLoss |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_IterationResult) GetEvalLoss() *wrappers.DoubleValue { |
| if m != nil { |
| return m.EvalLoss |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_IterationResult) GetLearnRate() float64 { |
| if m != nil { |
| return m.LearnRate |
| } |
| return 0 |
| } |
| |
| func (m *Model_TrainingRun_IterationResult) GetClusterInfos() []*Model_TrainingRun_IterationResult_ClusterInfo { |
| if m != nil { |
| return m.ClusterInfos |
| } |
| return nil |
| } |
| |
| // 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 *wrappers.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 *wrappers.Int64Value `protobuf:"bytes,3,opt,name=cluster_size,json=clusterSize,proto3" json:"cluster_size,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) Reset() { |
| *m = Model_TrainingRun_IterationResult_ClusterInfo{} |
| } |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*Model_TrainingRun_IterationResult_ClusterInfo) ProtoMessage() {} |
| func (*Model_TrainingRun_IterationResult_ClusterInfo) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{0, 7, 1, 0} |
| } |
| |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_Model_TrainingRun_IterationResult_ClusterInfo.Unmarshal(m, b) |
| } |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_Model_TrainingRun_IterationResult_ClusterInfo.Marshal(b, m, deterministic) |
| } |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_Model_TrainingRun_IterationResult_ClusterInfo.Merge(m, src) |
| } |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) XXX_Size() int { |
| return xxx_messageInfo_Model_TrainingRun_IterationResult_ClusterInfo.Size(m) |
| } |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) XXX_DiscardUnknown() { |
| xxx_messageInfo_Model_TrainingRun_IterationResult_ClusterInfo.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_Model_TrainingRun_IterationResult_ClusterInfo proto.InternalMessageInfo |
| |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) GetCentroidId() int64 { |
| if m != nil { |
| return m.CentroidId |
| } |
| return 0 |
| } |
| |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) GetClusterRadius() *wrappers.DoubleValue { |
| if m != nil { |
| return m.ClusterRadius |
| } |
| return nil |
| } |
| |
| func (m *Model_TrainingRun_IterationResult_ClusterInfo) GetClusterSize() *wrappers.Int64Value { |
| if m != nil { |
| return m.ClusterSize |
| } |
| return nil |
| } |
| |
| 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *GetModelRequest) Reset() { *m = GetModelRequest{} } |
| func (m *GetModelRequest) String() string { return proto.CompactTextString(m) } |
| func (*GetModelRequest) ProtoMessage() {} |
| func (*GetModelRequest) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{1} |
| } |
| |
| func (m *GetModelRequest) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_GetModelRequest.Unmarshal(m, b) |
| } |
| func (m *GetModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_GetModelRequest.Marshal(b, m, deterministic) |
| } |
| func (m *GetModelRequest) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_GetModelRequest.Merge(m, src) |
| } |
| func (m *GetModelRequest) XXX_Size() int { |
| return xxx_messageInfo_GetModelRequest.Size(m) |
| } |
| func (m *GetModelRequest) XXX_DiscardUnknown() { |
| xxx_messageInfo_GetModelRequest.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_GetModelRequest proto.InternalMessageInfo |
| |
| func (m *GetModelRequest) GetProjectId() string { |
| if m != nil { |
| return m.ProjectId |
| } |
| return "" |
| } |
| |
| func (m *GetModelRequest) GetDatasetId() string { |
| if m != nil { |
| return m.DatasetId |
| } |
| return "" |
| } |
| |
| func (m *GetModelRequest) GetModelId() string { |
| if m != nil { |
| return m.ModelId |
| } |
| return "" |
| } |
| |
| 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *PatchModelRequest) Reset() { *m = PatchModelRequest{} } |
| func (m *PatchModelRequest) String() string { return proto.CompactTextString(m) } |
| func (*PatchModelRequest) ProtoMessage() {} |
| func (*PatchModelRequest) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{2} |
| } |
| |
| func (m *PatchModelRequest) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_PatchModelRequest.Unmarshal(m, b) |
| } |
| func (m *PatchModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_PatchModelRequest.Marshal(b, m, deterministic) |
| } |
| func (m *PatchModelRequest) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_PatchModelRequest.Merge(m, src) |
| } |
| func (m *PatchModelRequest) XXX_Size() int { |
| return xxx_messageInfo_PatchModelRequest.Size(m) |
| } |
| func (m *PatchModelRequest) XXX_DiscardUnknown() { |
| xxx_messageInfo_PatchModelRequest.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_PatchModelRequest proto.InternalMessageInfo |
| |
| func (m *PatchModelRequest) GetProjectId() string { |
| if m != nil { |
| return m.ProjectId |
| } |
| return "" |
| } |
| |
| func (m *PatchModelRequest) GetDatasetId() string { |
| if m != nil { |
| return m.DatasetId |
| } |
| return "" |
| } |
| |
| func (m *PatchModelRequest) GetModelId() string { |
| if m != nil { |
| return m.ModelId |
| } |
| return "" |
| } |
| |
| func (m *PatchModelRequest) GetModel() *Model { |
| if m != nil { |
| return m.Model |
| } |
| return nil |
| } |
| |
| 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *DeleteModelRequest) Reset() { *m = DeleteModelRequest{} } |
| func (m *DeleteModelRequest) String() string { return proto.CompactTextString(m) } |
| func (*DeleteModelRequest) ProtoMessage() {} |
| func (*DeleteModelRequest) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{3} |
| } |
| |
| func (m *DeleteModelRequest) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_DeleteModelRequest.Unmarshal(m, b) |
| } |
| func (m *DeleteModelRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_DeleteModelRequest.Marshal(b, m, deterministic) |
| } |
| func (m *DeleteModelRequest) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_DeleteModelRequest.Merge(m, src) |
| } |
| func (m *DeleteModelRequest) XXX_Size() int { |
| return xxx_messageInfo_DeleteModelRequest.Size(m) |
| } |
| func (m *DeleteModelRequest) XXX_DiscardUnknown() { |
| xxx_messageInfo_DeleteModelRequest.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_DeleteModelRequest proto.InternalMessageInfo |
| |
| func (m *DeleteModelRequest) GetProjectId() string { |
| if m != nil { |
| return m.ProjectId |
| } |
| return "" |
| } |
| |
| func (m *DeleteModelRequest) GetDatasetId() string { |
| if m != nil { |
| return m.DatasetId |
| } |
| return "" |
| } |
| |
| func (m *DeleteModelRequest) GetModelId() string { |
| if m != nil { |
| return m.ModelId |
| } |
| return "" |
| } |
| |
| 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 *wrappers.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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ListModelsRequest) Reset() { *m = ListModelsRequest{} } |
| func (m *ListModelsRequest) String() string { return proto.CompactTextString(m) } |
| func (*ListModelsRequest) ProtoMessage() {} |
| func (*ListModelsRequest) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{4} |
| } |
| |
| func (m *ListModelsRequest) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ListModelsRequest.Unmarshal(m, b) |
| } |
| func (m *ListModelsRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ListModelsRequest.Marshal(b, m, deterministic) |
| } |
| func (m *ListModelsRequest) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ListModelsRequest.Merge(m, src) |
| } |
| func (m *ListModelsRequest) XXX_Size() int { |
| return xxx_messageInfo_ListModelsRequest.Size(m) |
| } |
| func (m *ListModelsRequest) XXX_DiscardUnknown() { |
| xxx_messageInfo_ListModelsRequest.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ListModelsRequest proto.InternalMessageInfo |
| |
| func (m *ListModelsRequest) GetProjectId() string { |
| if m != nil { |
| return m.ProjectId |
| } |
| return "" |
| } |
| |
| func (m *ListModelsRequest) GetDatasetId() string { |
| if m != nil { |
| return m.DatasetId |
| } |
| return "" |
| } |
| |
| func (m *ListModelsRequest) GetMaxResults() *wrappers.UInt32Value { |
| if m != nil { |
| return m.MaxResults |
| } |
| return nil |
| } |
| |
| func (m *ListModelsRequest) GetPageToken() string { |
| if m != nil { |
| return m.PageToken |
| } |
| return "" |
| } |
| |
| 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ListModelsResponse) Reset() { *m = ListModelsResponse{} } |
| func (m *ListModelsResponse) String() string { return proto.CompactTextString(m) } |
| func (*ListModelsResponse) ProtoMessage() {} |
| func (*ListModelsResponse) Descriptor() ([]byte, []int) { |
| return fileDescriptor_0bf703c728e4b09a, []int{5} |
| } |
| |
| func (m *ListModelsResponse) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ListModelsResponse.Unmarshal(m, b) |
| } |
| func (m *ListModelsResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ListModelsResponse.Marshal(b, m, deterministic) |
| } |
| func (m *ListModelsResponse) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ListModelsResponse.Merge(m, src) |
| } |
| func (m *ListModelsResponse) XXX_Size() int { |
| return xxx_messageInfo_ListModelsResponse.Size(m) |
| } |
| func (m *ListModelsResponse) XXX_DiscardUnknown() { |
| xxx_messageInfo_ListModelsResponse.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ListModelsResponse proto.InternalMessageInfo |
| |
| func (m *ListModelsResponse) GetModels() []*Model { |
| if m != nil { |
| return m.Models |
| } |
| return nil |
| } |
| |
| func (m *ListModelsResponse) GetNextPageToken() string { |
| if m != nil { |
| return m.NextPageToken |
| } |
| return "" |
| } |
| |
| func init() { |
| proto.RegisterEnum("google.cloud.bigquery.v2.Model_ModelType", Model_ModelType_name, Model_ModelType_value) |
| proto.RegisterEnum("google.cloud.bigquery.v2.Model_LossType", Model_LossType_name, Model_LossType_value) |
| proto.RegisterEnum("google.cloud.bigquery.v2.Model_DistanceType", Model_DistanceType_name, Model_DistanceType_value) |
| proto.RegisterEnum("google.cloud.bigquery.v2.Model_DataSplitMethod", Model_DataSplitMethod_name, Model_DataSplitMethod_value) |
| proto.RegisterEnum("google.cloud.bigquery.v2.Model_LearnRateStrategy", Model_LearnRateStrategy_name, Model_LearnRateStrategy_value) |
| proto.RegisterEnum("google.cloud.bigquery.v2.Model_OptimizationStrategy", Model_OptimizationStrategy_name, Model_OptimizationStrategy_value) |
| proto.RegisterEnum("google.cloud.bigquery.v2.Model_KmeansEnums_KmeansInitializationMethod", Model_KmeansEnums_KmeansInitializationMethod_name, Model_KmeansEnums_KmeansInitializationMethod_value) |
| proto.RegisterType((*Model)(nil), "google.cloud.bigquery.v2.Model") |
| proto.RegisterMapType((map[string]string)(nil), "google.cloud.bigquery.v2.Model.LabelsEntry") |
| proto.RegisterType((*Model_KmeansEnums)(nil), "google.cloud.bigquery.v2.Model.KmeansEnums") |
| proto.RegisterType((*Model_RegressionMetrics)(nil), "google.cloud.bigquery.v2.Model.RegressionMetrics") |
| proto.RegisterType((*Model_AggregateClassificationMetrics)(nil), "google.cloud.bigquery.v2.Model.AggregateClassificationMetrics") |
| proto.RegisterType((*Model_BinaryClassificationMetrics)(nil), "google.cloud.bigquery.v2.Model.BinaryClassificationMetrics") |
| proto.RegisterType((*Model_BinaryClassificationMetrics_BinaryConfusionMatrix)(nil), "google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix") |
| proto.RegisterType((*Model_MultiClassClassificationMetrics)(nil), "google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics") |
| proto.RegisterType((*Model_MultiClassClassificationMetrics_ConfusionMatrix)(nil), "google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix") |
| proto.RegisterType((*Model_MultiClassClassificationMetrics_ConfusionMatrix_Entry)(nil), "google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry") |
| proto.RegisterType((*Model_MultiClassClassificationMetrics_ConfusionMatrix_Row)(nil), "google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Row") |
| proto.RegisterType((*Model_ClusteringMetrics)(nil), "google.cloud.bigquery.v2.Model.ClusteringMetrics") |
| proto.RegisterType((*Model_ClusteringMetrics_Cluster)(nil), "google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster") |
| proto.RegisterType((*Model_ClusteringMetrics_Cluster_FeatureValue)(nil), "google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue") |
| proto.RegisterType((*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue)(nil), "google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue") |
| proto.RegisterType((*Model_ClusteringMetrics_Cluster_FeatureValue_CategoricalValue_CategoryCount)(nil), "google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount") |
| proto.RegisterType((*Model_EvaluationMetrics)(nil), "google.cloud.bigquery.v2.Model.EvaluationMetrics") |
| proto.RegisterType((*Model_TrainingRun)(nil), "google.cloud.bigquery.v2.Model.TrainingRun") |
| proto.RegisterType((*Model_TrainingRun_TrainingOptions)(nil), "google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions") |
| proto.RegisterMapType((map[string]float64)(nil), "google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.LabelClassWeightsEntry") |
| proto.RegisterType((*Model_TrainingRun_IterationResult)(nil), "google.cloud.bigquery.v2.Model.TrainingRun.IterationResult") |
| proto.RegisterType((*Model_TrainingRun_IterationResult_ClusterInfo)(nil), "google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.ClusterInfo") |
| proto.RegisterType((*GetModelRequest)(nil), "google.cloud.bigquery.v2.GetModelRequest") |
| proto.RegisterType((*PatchModelRequest)(nil), "google.cloud.bigquery.v2.PatchModelRequest") |
| proto.RegisterType((*DeleteModelRequest)(nil), "google.cloud.bigquery.v2.DeleteModelRequest") |
| proto.RegisterType((*ListModelsRequest)(nil), "google.cloud.bigquery.v2.ListModelsRequest") |
| proto.RegisterType((*ListModelsResponse)(nil), "google.cloud.bigquery.v2.ListModelsResponse") |
| } |
| |
| func init() { |
| proto.RegisterFile("google/cloud/bigquery/v2/model.proto", fileDescriptor_0bf703c728e4b09a) |
| } |
| |
| var fileDescriptor_0bf703c728e4b09a = []byte{ |
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| 0x7f, 0x17, 0x3b, 0xa2, 0xb4, 0x4b, 0x1e, 0x6c, 0x6c, 0x9c, 0x9c, 0x9c, 0x8c, 0x8c, 0x6e, 0xe8, |
| 0x01, 0x3d, 0xea, 0xff, 0xce, 0x6c, 0x7d, 0x5a, 0xc6, 0x82, 0x8f, 0x75, 0xd3, 0x73, 0xed, 0xc9, |
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| 0xfb, 0xf8, 0x7d, 0xc9, 0xd5, 0xf1, 0x6c, 0xdd, 0xed, 0x14, 0x3c, 0xbf, 0xb3, 0xd1, 0xc1, 0x2e, |
| 0x77, 0xeb, 0xc6, 0x60, 0xd2, 0xf1, 0xdf, 0xd8, 0xbd, 0x1b, 0x3e, 0x1f, 0xce, 0x71, 0xe6, 0x37, |
| 0xff, 0x15, 0x00, 0x00, 0xff, 0xff, 0x16, 0x79, 0x2e, 0xe2, 0x6b, 0x28, 0x00, 0x00, |
| } |
| |
| // Reference imports to suppress errors if they are not otherwise used. |
| var _ context.Context |
| var _ grpc.ClientConnInterface |
| |
| // This is a compile-time assertion to ensure that this generated file |
| // is compatible with the grpc package it is being compiled against. |
| const _ = grpc.SupportPackageIsVersion6 |
| |
| // 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. |
| 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) (*empty.Empty, error) |
| } |
| |
| type modelServiceClient struct { |
| cc grpc.ClientConnInterface |
| } |
| |
| func NewModelServiceClient(cc grpc.ClientConnInterface) ModelServiceClient { |
| return &modelServiceClient{cc} |
| } |
| |
| func (c *modelServiceClient) GetModel(ctx context.Context, in *GetModelRequest, opts ...grpc.CallOption) (*Model, error) { |
| out := new(Model) |
| err := c.cc.Invoke(ctx, "/google.cloud.bigquery.v2.ModelService/GetModel", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| func (c *modelServiceClient) ListModels(ctx context.Context, in *ListModelsRequest, opts ...grpc.CallOption) (*ListModelsResponse, error) { |
| out := new(ListModelsResponse) |
| err := c.cc.Invoke(ctx, "/google.cloud.bigquery.v2.ModelService/ListModels", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| func (c *modelServiceClient) PatchModel(ctx context.Context, in *PatchModelRequest, opts ...grpc.CallOption) (*Model, error) { |
| out := new(Model) |
| err := c.cc.Invoke(ctx, "/google.cloud.bigquery.v2.ModelService/PatchModel", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| func (c *modelServiceClient) DeleteModel(ctx context.Context, in *DeleteModelRequest, opts ...grpc.CallOption) (*empty.Empty, error) { |
| out := new(empty.Empty) |
| err := c.cc.Invoke(ctx, "/google.cloud.bigquery.v2.ModelService/DeleteModel", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| // 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. |
| 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) (*empty.Empty, error) |
| } |
| |
| // UnimplementedModelServiceServer can be embedded to have forward compatible implementations. |
| type UnimplementedModelServiceServer struct { |
| } |
| |
| func (*UnimplementedModelServiceServer) GetModel(ctx context.Context, req *GetModelRequest) (*Model, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method GetModel not implemented") |
| } |
| func (*UnimplementedModelServiceServer) ListModels(ctx context.Context, req *ListModelsRequest) (*ListModelsResponse, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method ListModels not implemented") |
| } |
| func (*UnimplementedModelServiceServer) PatchModel(ctx context.Context, req *PatchModelRequest) (*Model, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method PatchModel not implemented") |
| } |
| func (*UnimplementedModelServiceServer) DeleteModel(ctx context.Context, req *DeleteModelRequest) (*empty.Empty, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method DeleteModel not implemented") |
| } |
| |
| func RegisterModelServiceServer(s *grpc.Server, srv ModelServiceServer) { |
| s.RegisterService(&_ModelService_serviceDesc, srv) |
| } |
| |
| func _ModelService_GetModel_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(GetModelRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(ModelServiceServer).GetModel(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.bigquery.v2.ModelService/GetModel", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(ModelServiceServer).GetModel(ctx, req.(*GetModelRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| func _ModelService_ListModels_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(ListModelsRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(ModelServiceServer).ListModels(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.bigquery.v2.ModelService/ListModels", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(ModelServiceServer).ListModels(ctx, req.(*ListModelsRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| func _ModelService_PatchModel_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(PatchModelRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(ModelServiceServer).PatchModel(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.bigquery.v2.ModelService/PatchModel", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(ModelServiceServer).PatchModel(ctx, req.(*PatchModelRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| func _ModelService_DeleteModel_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(DeleteModelRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(ModelServiceServer).DeleteModel(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.bigquery.v2.ModelService/DeleteModel", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(ModelServiceServer).DeleteModel(ctx, req.(*DeleteModelRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| var _ModelService_serviceDesc = grpc.ServiceDesc{ |
| ServiceName: "google.cloud.bigquery.v2.ModelService", |
| HandlerType: (*ModelServiceServer)(nil), |
| Methods: []grpc.MethodDesc{ |
| { |
| MethodName: "GetModel", |
| Handler: _ModelService_GetModel_Handler, |
| }, |
| { |
| MethodName: "ListModels", |
| Handler: _ModelService_ListModels_Handler, |
| }, |
| { |
| MethodName: "PatchModel", |
| Handler: _ModelService_PatchModel_Handler, |
| }, |
| { |
| MethodName: "DeleteModel", |
| Handler: _ModelService_DeleteModel_Handler, |
| }, |
| }, |
| Streams: []grpc.StreamDesc{}, |
| Metadata: "google/cloud/bigquery/v2/model.proto", |
| } |