| // Copyright 2020 Google LLC |
| // |
| // Licensed under the Apache License, Version 2.0 (the "License"); |
| // you may not use this file except in compliance with the License. |
| // You may obtain a copy of the License at |
| // |
| // http://www.apache.org/licenses/LICENSE-2.0 |
| // |
| // Unless required by applicable law or agreed to in writing, software |
| // distributed under the License is distributed on an "AS IS" BASIS, |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| // See the License for the specific language governing permissions and |
| // limitations under the License. |
| |
| // Code generated by protoc-gen-go. DO NOT EDIT. |
| // versions: |
| // protoc-gen-go v1.22.0 |
| // protoc v3.12.2 |
| // source: google/cloud/automl/v1beta1/tables.proto |
| |
| package automl |
| |
| import ( |
| reflect "reflect" |
| sync "sync" |
| |
| proto "github.com/golang/protobuf/proto" |
| _struct "github.com/golang/protobuf/ptypes/struct" |
| timestamp "github.com/golang/protobuf/ptypes/timestamp" |
| _ "google.golang.org/genproto/googleapis/api/annotations" |
| protoreflect "google.golang.org/protobuf/reflect/protoreflect" |
| protoimpl "google.golang.org/protobuf/runtime/protoimpl" |
| ) |
| |
| const ( |
| // Verify that this generated code is sufficiently up-to-date. |
| _ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion) |
| // Verify that runtime/protoimpl is sufficiently up-to-date. |
| _ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20) |
| ) |
| |
| // This is a compile-time assertion that a sufficiently up-to-date version |
| // of the legacy proto package is being used. |
| const _ = proto.ProtoPackageIsVersion4 |
| |
| // Metadata for a dataset used for AutoML Tables. |
| type TablesDatasetMetadata struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Output only. The table_spec_id of the primary table of this dataset. |
| PrimaryTableSpecId string `protobuf:"bytes,1,opt,name=primary_table_spec_id,json=primaryTableSpecId,proto3" json:"primary_table_spec_id,omitempty"` |
| // column_spec_id of the primary table's column that should be used as the |
| // training & prediction target. |
| // This column must be non-nullable and have one of following data types |
| // (otherwise model creation will error): |
| // |
| // * CATEGORY |
| // |
| // * FLOAT64 |
| // |
| // If the type is CATEGORY , only up to |
| // 100 unique values may exist in that column across all rows. |
| // |
| // NOTE: Updates of this field will instantly affect any other users |
| // concurrently working with the dataset. |
| TargetColumnSpecId string `protobuf:"bytes,2,opt,name=target_column_spec_id,json=targetColumnSpecId,proto3" json:"target_column_spec_id,omitempty"` |
| // column_spec_id of the primary table's column that should be used as the |
| // weight column, i.e. the higher the value the more important the row will be |
| // during model training. |
| // Required type: FLOAT64. |
| // Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is |
| // ignored for training. |
| // If not set all rows are assumed to have equal weight of 1. |
| // NOTE: Updates of this field will instantly affect any other users |
| // concurrently working with the dataset. |
| WeightColumnSpecId string `protobuf:"bytes,3,opt,name=weight_column_spec_id,json=weightColumnSpecId,proto3" json:"weight_column_spec_id,omitempty"` |
| // column_spec_id of the primary table column which specifies a possible ML |
| // use of the row, i.e. the column will be used to split the rows into TRAIN, |
| // VALIDATE and TEST sets. |
| // Required type: STRING. |
| // This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST` |
| // among its values, or only have `TEST`, `UNASSIGNED` values. In the latter |
| // case the rows with `UNASSIGNED` value will be assigned by AutoML. Note |
| // that if a given ml use distribution makes it impossible to create a "good" |
| // model, that call will error describing the issue. |
| // If both this column_spec_id and primary table's time_column_spec_id are not |
| // set, then all rows are treated as `UNASSIGNED`. |
| // NOTE: Updates of this field will instantly affect any other users |
| // concurrently working with the dataset. |
| MlUseColumnSpecId string `protobuf:"bytes,4,opt,name=ml_use_column_spec_id,json=mlUseColumnSpecId,proto3" json:"ml_use_column_spec_id,omitempty"` |
| // Output only. Correlations between |
| // |
| // [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id], |
| // and other columns of the |
| // |
| // [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id]. |
| // Only set if the target column is set. Mapping from other column spec id to |
| // its CorrelationStats with the target column. |
| // This field may be stale, see the stats_update_time field for |
| // for the timestamp at which these stats were last updated. |
| TargetColumnCorrelations map[string]*CorrelationStats `protobuf:"bytes,6,rep,name=target_column_correlations,json=targetColumnCorrelations,proto3" json:"target_column_correlations,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` |
| // Output only. The most recent timestamp when target_column_correlations |
| // field and all descendant ColumnSpec.data_stats and |
| // ColumnSpec.top_correlated_columns fields were last (re-)generated. Any |
| // changes that happened to the dataset afterwards are not reflected in these |
| // fields values. The regeneration happens in the background on a best effort |
| // basis. |
| StatsUpdateTime *timestamp.Timestamp `protobuf:"bytes,7,opt,name=stats_update_time,json=statsUpdateTime,proto3" json:"stats_update_time,omitempty"` |
| } |
| |
| func (x *TablesDatasetMetadata) Reset() { |
| *x = TablesDatasetMetadata{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1beta1_tables_proto_msgTypes[0] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *TablesDatasetMetadata) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*TablesDatasetMetadata) ProtoMessage() {} |
| |
| func (x *TablesDatasetMetadata) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1beta1_tables_proto_msgTypes[0] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use TablesDatasetMetadata.ProtoReflect.Descriptor instead. |
| func (*TablesDatasetMetadata) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1beta1_tables_proto_rawDescGZIP(), []int{0} |
| } |
| |
| func (x *TablesDatasetMetadata) GetPrimaryTableSpecId() string { |
| if x != nil { |
| return x.PrimaryTableSpecId |
| } |
| return "" |
| } |
| |
| func (x *TablesDatasetMetadata) GetTargetColumnSpecId() string { |
| if x != nil { |
| return x.TargetColumnSpecId |
| } |
| return "" |
| } |
| |
| func (x *TablesDatasetMetadata) GetWeightColumnSpecId() string { |
| if x != nil { |
| return x.WeightColumnSpecId |
| } |
| return "" |
| } |
| |
| func (x *TablesDatasetMetadata) GetMlUseColumnSpecId() string { |
| if x != nil { |
| return x.MlUseColumnSpecId |
| } |
| return "" |
| } |
| |
| func (x *TablesDatasetMetadata) GetTargetColumnCorrelations() map[string]*CorrelationStats { |
| if x != nil { |
| return x.TargetColumnCorrelations |
| } |
| return nil |
| } |
| |
| func (x *TablesDatasetMetadata) GetStatsUpdateTime() *timestamp.Timestamp { |
| if x != nil { |
| return x.StatsUpdateTime |
| } |
| return nil |
| } |
| |
| // Model metadata specific to AutoML Tables. |
| type TablesModelMetadata struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Additional optimization objective configuration. Required for |
| // `MAXIMIZE_PRECISION_AT_RECALL` and `MAXIMIZE_RECALL_AT_PRECISION`, |
| // otherwise unused. |
| // |
| // Types that are assignable to AdditionalOptimizationObjectiveConfig: |
| // *TablesModelMetadata_OptimizationObjectiveRecallValue |
| // *TablesModelMetadata_OptimizationObjectivePrecisionValue |
| AdditionalOptimizationObjectiveConfig isTablesModelMetadata_AdditionalOptimizationObjectiveConfig `protobuf_oneof:"additional_optimization_objective_config"` |
| // Column spec of the dataset's primary table's column the model is |
| // predicting. Snapshotted when model creation started. |
| // Only 3 fields are used: |
| // name - May be set on CreateModel, if it's not then the ColumnSpec |
| // corresponding to the current target_column_spec_id of the dataset |
| // the model is trained from is used. |
| // If neither is set, CreateModel will error. |
| // display_name - Output only. |
| // data_type - Output only. |
| TargetColumnSpec *ColumnSpec `protobuf:"bytes,2,opt,name=target_column_spec,json=targetColumnSpec,proto3" json:"target_column_spec,omitempty"` |
| // Column specs of the dataset's primary table's columns, on which |
| // the model is trained and which are used as the input for predictions. |
| // The |
| // |
| // [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] |
| // as well as, according to dataset's state upon model creation, |
| // |
| // [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id], |
| // and |
| // |
| // [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] |
| // must never be included here. |
| // |
| // Only 3 fields are used: |
| // |
| // * name - May be set on CreateModel, if set only the columns specified are |
| // used, otherwise all primary table's columns (except the ones listed |
| // above) are used for the training and prediction input. |
| // |
| // * display_name - Output only. |
| // |
| // * data_type - Output only. |
| InputFeatureColumnSpecs []*ColumnSpec `protobuf:"bytes,3,rep,name=input_feature_column_specs,json=inputFeatureColumnSpecs,proto3" json:"input_feature_column_specs,omitempty"` |
| // Objective function the model is optimizing towards. The training process |
| // creates a model that maximizes/minimizes the value of the objective |
| // function over the validation set. |
| // |
| // The supported optimization objectives depend on the prediction type. |
| // If the field is not set, a default objective function is used. |
| // |
| // CLASSIFICATION_BINARY: |
| // "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver |
| // operating characteristic (ROC) curve. |
| // "MINIMIZE_LOG_LOSS" - Minimize log loss. |
| // "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve. |
| // "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified |
| // recall value. |
| // "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified |
| // precision value. |
| // |
| // CLASSIFICATION_MULTI_CLASS : |
| // "MINIMIZE_LOG_LOSS" (default) - Minimize log loss. |
| // |
| // |
| // REGRESSION: |
| // "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE). |
| // "MINIMIZE_MAE" - Minimize mean-absolute error (MAE). |
| // "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE). |
| OptimizationObjective string `protobuf:"bytes,4,opt,name=optimization_objective,json=optimizationObjective,proto3" json:"optimization_objective,omitempty"` |
| // Output only. Auxiliary information for each of the |
| // input_feature_column_specs with respect to this particular model. |
| TablesModelColumnInfo []*TablesModelColumnInfo `protobuf:"bytes,5,rep,name=tables_model_column_info,json=tablesModelColumnInfo,proto3" json:"tables_model_column_info,omitempty"` |
| // Required. The train budget of creating this model, expressed in milli node |
| // hours i.e. 1,000 value in this field means 1 node hour. |
| // |
| // The training cost of the model will not exceed this budget. The final cost |
| // will be attempted to be close to the budget, though may end up being (even) |
| // noticeably smaller - at the backend's discretion. This especially may |
| // happen when further model training ceases to provide any improvements. |
| // |
| // If the budget is set to a value known to be insufficient to train a |
| // model for the given dataset, the training won't be attempted and |
| // will error. |
| // |
| // The train budget must be between 1,000 and 72,000 milli node hours, |
| // inclusive. |
| TrainBudgetMilliNodeHours int64 `protobuf:"varint,6,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"` |
| // Output only. The actual training cost of the model, expressed in milli |
| // node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed |
| // to not exceed the train budget. |
| TrainCostMilliNodeHours int64 `protobuf:"varint,7,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,omitempty"` |
| // Use the entire training budget. This disables the early stopping feature. |
| // By default, the early stopping feature is enabled, which means that AutoML |
| // Tables might stop training before the entire training budget has been used. |
| DisableEarlyStopping bool `protobuf:"varint,12,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"` |
| } |
| |
| func (x *TablesModelMetadata) Reset() { |
| *x = TablesModelMetadata{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1beta1_tables_proto_msgTypes[1] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *TablesModelMetadata) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*TablesModelMetadata) ProtoMessage() {} |
| |
| func (x *TablesModelMetadata) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1beta1_tables_proto_msgTypes[1] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use TablesModelMetadata.ProtoReflect.Descriptor instead. |
| func (*TablesModelMetadata) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1beta1_tables_proto_rawDescGZIP(), []int{1} |
| } |
| |
| func (m *TablesModelMetadata) GetAdditionalOptimizationObjectiveConfig() isTablesModelMetadata_AdditionalOptimizationObjectiveConfig { |
| if m != nil { |
| return m.AdditionalOptimizationObjectiveConfig |
| } |
| return nil |
| } |
| |
| func (x *TablesModelMetadata) GetOptimizationObjectiveRecallValue() float32 { |
| if x, ok := x.GetAdditionalOptimizationObjectiveConfig().(*TablesModelMetadata_OptimizationObjectiveRecallValue); ok { |
| return x.OptimizationObjectiveRecallValue |
| } |
| return 0 |
| } |
| |
| func (x *TablesModelMetadata) GetOptimizationObjectivePrecisionValue() float32 { |
| if x, ok := x.GetAdditionalOptimizationObjectiveConfig().(*TablesModelMetadata_OptimizationObjectivePrecisionValue); ok { |
| return x.OptimizationObjectivePrecisionValue |
| } |
| return 0 |
| } |
| |
| func (x *TablesModelMetadata) GetTargetColumnSpec() *ColumnSpec { |
| if x != nil { |
| return x.TargetColumnSpec |
| } |
| return nil |
| } |
| |
| func (x *TablesModelMetadata) GetInputFeatureColumnSpecs() []*ColumnSpec { |
| if x != nil { |
| return x.InputFeatureColumnSpecs |
| } |
| return nil |
| } |
| |
| func (x *TablesModelMetadata) GetOptimizationObjective() string { |
| if x != nil { |
| return x.OptimizationObjective |
| } |
| return "" |
| } |
| |
| func (x *TablesModelMetadata) GetTablesModelColumnInfo() []*TablesModelColumnInfo { |
| if x != nil { |
| return x.TablesModelColumnInfo |
| } |
| return nil |
| } |
| |
| func (x *TablesModelMetadata) GetTrainBudgetMilliNodeHours() int64 { |
| if x != nil { |
| return x.TrainBudgetMilliNodeHours |
| } |
| return 0 |
| } |
| |
| func (x *TablesModelMetadata) GetTrainCostMilliNodeHours() int64 { |
| if x != nil { |
| return x.TrainCostMilliNodeHours |
| } |
| return 0 |
| } |
| |
| func (x *TablesModelMetadata) GetDisableEarlyStopping() bool { |
| if x != nil { |
| return x.DisableEarlyStopping |
| } |
| return false |
| } |
| |
| type isTablesModelMetadata_AdditionalOptimizationObjectiveConfig interface { |
| isTablesModelMetadata_AdditionalOptimizationObjectiveConfig() |
| } |
| |
| type TablesModelMetadata_OptimizationObjectiveRecallValue struct { |
| // Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". |
| // Must be between 0 and 1, inclusive. |
| OptimizationObjectiveRecallValue float32 `protobuf:"fixed32,17,opt,name=optimization_objective_recall_value,json=optimizationObjectiveRecallValue,proto3,oneof"` |
| } |
| |
| type TablesModelMetadata_OptimizationObjectivePrecisionValue struct { |
| // Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". |
| // Must be between 0 and 1, inclusive. |
| OptimizationObjectivePrecisionValue float32 `protobuf:"fixed32,18,opt,name=optimization_objective_precision_value,json=optimizationObjectivePrecisionValue,proto3,oneof"` |
| } |
| |
| func (*TablesModelMetadata_OptimizationObjectiveRecallValue) isTablesModelMetadata_AdditionalOptimizationObjectiveConfig() { |
| } |
| |
| func (*TablesModelMetadata_OptimizationObjectivePrecisionValue) isTablesModelMetadata_AdditionalOptimizationObjectiveConfig() { |
| } |
| |
| // Contains annotation details specific to Tables. |
| type TablesAnnotation struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher |
| // value means greater confidence in the returned value. |
| // For |
| // |
| // [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] |
| // of FLOAT64 data type the score is not populated. |
| Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"` |
| // Output only. Only populated when |
| // |
| // [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] |
| // has FLOAT64 data type. An interval in which the exactly correct target |
| // value has 95% chance to be in. |
| PredictionInterval *DoubleRange `protobuf:"bytes,4,opt,name=prediction_interval,json=predictionInterval,proto3" json:"prediction_interval,omitempty"` |
| // The predicted value of the row's |
| // |
| // [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]. |
| // The value depends on the column's DataType: |
| // |
| // * CATEGORY - the predicted (with the above confidence `score`) CATEGORY |
| // value. |
| // |
| // * FLOAT64 - the predicted (with above `prediction_interval`) FLOAT64 value. |
| Value *_struct.Value `protobuf:"bytes,2,opt,name=value,proto3" json:"value,omitempty"` |
| // Output only. Auxiliary information for each of the model's |
| // |
| // [input_feature_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs] |
| // with respect to this particular prediction. |
| // If no other fields than |
| // |
| // [column_spec_name][google.cloud.automl.v1beta1.TablesModelColumnInfo.column_spec_name] |
| // and |
| // |
| // [column_display_name][google.cloud.automl.v1beta1.TablesModelColumnInfo.column_display_name] |
| // would be populated, then this whole field is not. |
| TablesModelColumnInfo []*TablesModelColumnInfo `protobuf:"bytes,3,rep,name=tables_model_column_info,json=tablesModelColumnInfo,proto3" json:"tables_model_column_info,omitempty"` |
| // Output only. Stores the prediction score for the baseline example, which |
| // is defined as the example with all values set to their baseline values. |
| // This is used as part of the Sampled Shapley explanation of the model's |
| // prediction. This field is populated only when feature importance is |
| // requested. For regression models, this holds the baseline prediction for |
| // the baseline example. For classification models, this holds the baseline |
| // prediction for the baseline example for the argmax class. |
| BaselineScore float32 `protobuf:"fixed32,5,opt,name=baseline_score,json=baselineScore,proto3" json:"baseline_score,omitempty"` |
| } |
| |
| func (x *TablesAnnotation) Reset() { |
| *x = TablesAnnotation{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1beta1_tables_proto_msgTypes[2] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *TablesAnnotation) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*TablesAnnotation) ProtoMessage() {} |
| |
| func (x *TablesAnnotation) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1beta1_tables_proto_msgTypes[2] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use TablesAnnotation.ProtoReflect.Descriptor instead. |
| func (*TablesAnnotation) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1beta1_tables_proto_rawDescGZIP(), []int{2} |
| } |
| |
| func (x *TablesAnnotation) GetScore() float32 { |
| if x != nil { |
| return x.Score |
| } |
| return 0 |
| } |
| |
| func (x *TablesAnnotation) GetPredictionInterval() *DoubleRange { |
| if x != nil { |
| return x.PredictionInterval |
| } |
| return nil |
| } |
| |
| func (x *TablesAnnotation) GetValue() *_struct.Value { |
| if x != nil { |
| return x.Value |
| } |
| return nil |
| } |
| |
| func (x *TablesAnnotation) GetTablesModelColumnInfo() []*TablesModelColumnInfo { |
| if x != nil { |
| return x.TablesModelColumnInfo |
| } |
| return nil |
| } |
| |
| func (x *TablesAnnotation) GetBaselineScore() float32 { |
| if x != nil { |
| return x.BaselineScore |
| } |
| return 0 |
| } |
| |
| // An information specific to given column and Tables Model, in context |
| // of the Model and the predictions created by it. |
| type TablesModelColumnInfo struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Output only. The name of the ColumnSpec describing the column. Not |
| // populated when this proto is outputted to BigQuery. |
| ColumnSpecName string `protobuf:"bytes,1,opt,name=column_spec_name,json=columnSpecName,proto3" json:"column_spec_name,omitempty"` |
| // Output only. The display name of the column (same as the display_name of |
| // its ColumnSpec). |
| ColumnDisplayName string `protobuf:"bytes,2,opt,name=column_display_name,json=columnDisplayName,proto3" json:"column_display_name,omitempty"` |
| // Output only. When given as part of a Model (always populated): |
| // Measurement of how much model predictions correctness on the TEST data |
| // depend on values in this column. A value between 0 and 1, higher means |
| // higher influence. These values are normalized - for all input feature |
| // columns of a given model they add to 1. |
| // |
| // When given back by Predict (populated iff |
| // [feature_importance |
| // param][google.cloud.automl.v1beta1.PredictRequest.params] is set) or Batch |
| // Predict (populated iff |
| // [feature_importance][google.cloud.automl.v1beta1.PredictRequest.params] |
| // param is set): |
| // Measurement of how impactful for the prediction returned for the given row |
| // the value in this column was. Specifically, the feature importance |
| // specifies the marginal contribution that the feature made to the prediction |
| // score compared to the baseline score. These values are computed using the |
| // Sampled Shapley method. |
| FeatureImportance float32 `protobuf:"fixed32,3,opt,name=feature_importance,json=featureImportance,proto3" json:"feature_importance,omitempty"` |
| } |
| |
| func (x *TablesModelColumnInfo) Reset() { |
| *x = TablesModelColumnInfo{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1beta1_tables_proto_msgTypes[3] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *TablesModelColumnInfo) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*TablesModelColumnInfo) ProtoMessage() {} |
| |
| func (x *TablesModelColumnInfo) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1beta1_tables_proto_msgTypes[3] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use TablesModelColumnInfo.ProtoReflect.Descriptor instead. |
| func (*TablesModelColumnInfo) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1beta1_tables_proto_rawDescGZIP(), []int{3} |
| } |
| |
| func (x *TablesModelColumnInfo) GetColumnSpecName() string { |
| if x != nil { |
| return x.ColumnSpecName |
| } |
| return "" |
| } |
| |
| func (x *TablesModelColumnInfo) GetColumnDisplayName() string { |
| if x != nil { |
| return x.ColumnDisplayName |
| } |
| return "" |
| } |
| |
| func (x *TablesModelColumnInfo) GetFeatureImportance() float32 { |
| if x != nil { |
| return x.FeatureImportance |
| } |
| return 0 |
| } |
| |
| var File_google_cloud_automl_v1beta1_tables_proto protoreflect.FileDescriptor |
| |
| var file_google_cloud_automl_v1beta1_tables_proto_rawDesc = []byte{ |
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| } |
| |
| var ( |
| file_google_cloud_automl_v1beta1_tables_proto_rawDescOnce sync.Once |
| file_google_cloud_automl_v1beta1_tables_proto_rawDescData = file_google_cloud_automl_v1beta1_tables_proto_rawDesc |
| ) |
| |
| func file_google_cloud_automl_v1beta1_tables_proto_rawDescGZIP() []byte { |
| file_google_cloud_automl_v1beta1_tables_proto_rawDescOnce.Do(func() { |
| file_google_cloud_automl_v1beta1_tables_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_automl_v1beta1_tables_proto_rawDescData) |
| }) |
| return file_google_cloud_automl_v1beta1_tables_proto_rawDescData |
| } |
| |
| var file_google_cloud_automl_v1beta1_tables_proto_msgTypes = make([]protoimpl.MessageInfo, 5) |
| var file_google_cloud_automl_v1beta1_tables_proto_goTypes = []interface{}{ |
| (*TablesDatasetMetadata)(nil), // 0: google.cloud.automl.v1beta1.TablesDatasetMetadata |
| (*TablesModelMetadata)(nil), // 1: google.cloud.automl.v1beta1.TablesModelMetadata |
| (*TablesAnnotation)(nil), // 2: google.cloud.automl.v1beta1.TablesAnnotation |
| (*TablesModelColumnInfo)(nil), // 3: google.cloud.automl.v1beta1.TablesModelColumnInfo |
| nil, // 4: google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry |
| (*timestamp.Timestamp)(nil), // 5: google.protobuf.Timestamp |
| (*ColumnSpec)(nil), // 6: google.cloud.automl.v1beta1.ColumnSpec |
| (*DoubleRange)(nil), // 7: google.cloud.automl.v1beta1.DoubleRange |
| (*_struct.Value)(nil), // 8: google.protobuf.Value |
| (*CorrelationStats)(nil), // 9: google.cloud.automl.v1beta1.CorrelationStats |
| } |
| var file_google_cloud_automl_v1beta1_tables_proto_depIdxs = []int32{ |
| 4, // 0: google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_correlations:type_name -> google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry |
| 5, // 1: google.cloud.automl.v1beta1.TablesDatasetMetadata.stats_update_time:type_name -> google.protobuf.Timestamp |
| 6, // 2: google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec:type_name -> google.cloud.automl.v1beta1.ColumnSpec |
| 6, // 3: google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs:type_name -> google.cloud.automl.v1beta1.ColumnSpec |
| 3, // 4: google.cloud.automl.v1beta1.TablesModelMetadata.tables_model_column_info:type_name -> google.cloud.automl.v1beta1.TablesModelColumnInfo |
| 7, // 5: google.cloud.automl.v1beta1.TablesAnnotation.prediction_interval:type_name -> google.cloud.automl.v1beta1.DoubleRange |
| 8, // 6: google.cloud.automl.v1beta1.TablesAnnotation.value:type_name -> google.protobuf.Value |
| 3, // 7: google.cloud.automl.v1beta1.TablesAnnotation.tables_model_column_info:type_name -> google.cloud.automl.v1beta1.TablesModelColumnInfo |
| 9, // 8: google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry.value:type_name -> google.cloud.automl.v1beta1.CorrelationStats |
| 9, // [9:9] is the sub-list for method output_type |
| 9, // [9:9] is the sub-list for method input_type |
| 9, // [9:9] is the sub-list for extension type_name |
| 9, // [9:9] is the sub-list for extension extendee |
| 0, // [0:9] is the sub-list for field type_name |
| } |
| |
| func init() { file_google_cloud_automl_v1beta1_tables_proto_init() } |
| func file_google_cloud_automl_v1beta1_tables_proto_init() { |
| if File_google_cloud_automl_v1beta1_tables_proto != nil { |
| return |
| } |
| file_google_cloud_automl_v1beta1_classification_proto_init() |
| file_google_cloud_automl_v1beta1_column_spec_proto_init() |
| file_google_cloud_automl_v1beta1_data_items_proto_init() |
| file_google_cloud_automl_v1beta1_data_stats_proto_init() |
| file_google_cloud_automl_v1beta1_ranges_proto_init() |
| file_google_cloud_automl_v1beta1_regression_proto_init() |
| file_google_cloud_automl_v1beta1_temporal_proto_init() |
| if !protoimpl.UnsafeEnabled { |
| file_google_cloud_automl_v1beta1_tables_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*TablesDatasetMetadata); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1beta1_tables_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*TablesModelMetadata); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1beta1_tables_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*TablesAnnotation); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1beta1_tables_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*TablesModelColumnInfo); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| } |
| file_google_cloud_automl_v1beta1_tables_proto_msgTypes[1].OneofWrappers = []interface{}{ |
| (*TablesModelMetadata_OptimizationObjectiveRecallValue)(nil), |
| (*TablesModelMetadata_OptimizationObjectivePrecisionValue)(nil), |
| } |
| type x struct{} |
| out := protoimpl.TypeBuilder{ |
| File: protoimpl.DescBuilder{ |
| GoPackagePath: reflect.TypeOf(x{}).PkgPath(), |
| RawDescriptor: file_google_cloud_automl_v1beta1_tables_proto_rawDesc, |
| NumEnums: 0, |
| NumMessages: 5, |
| NumExtensions: 0, |
| NumServices: 0, |
| }, |
| GoTypes: file_google_cloud_automl_v1beta1_tables_proto_goTypes, |
| DependencyIndexes: file_google_cloud_automl_v1beta1_tables_proto_depIdxs, |
| MessageInfos: file_google_cloud_automl_v1beta1_tables_proto_msgTypes, |
| }.Build() |
| File_google_cloud_automl_v1beta1_tables_proto = out.File |
| file_google_cloud_automl_v1beta1_tables_proto_rawDesc = nil |
| file_google_cloud_automl_v1beta1_tables_proto_goTypes = nil |
| file_google_cloud_automl_v1beta1_tables_proto_depIdxs = nil |
| } |
