// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/automl/v1beta1/tables.proto

package automl

import (
	fmt "fmt"
	math "math"

	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"
)

// 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

// Metadata for a dataset used for AutoML Tables.
type TablesDatasetMetadata struct {
	// 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"`
	XXX_NoUnkeyedLiteral struct{}             `json:"-"`
	XXX_unrecognized     []byte               `json:"-"`
	XXX_sizecache        int32                `json:"-"`
}

func (m *TablesDatasetMetadata) Reset()         { *m = TablesDatasetMetadata{} }
func (m *TablesDatasetMetadata) String() string { return proto.CompactTextString(m) }
func (*TablesDatasetMetadata) ProtoMessage()    {}
func (*TablesDatasetMetadata) Descriptor() ([]byte, []int) {
	return fileDescriptor_a4edb25480826735, []int{0}
}

func (m *TablesDatasetMetadata) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_TablesDatasetMetadata.Unmarshal(m, b)
}
func (m *TablesDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_TablesDatasetMetadata.Marshal(b, m, deterministic)
}
func (m *TablesDatasetMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TablesDatasetMetadata.Merge(m, src)
}
func (m *TablesDatasetMetadata) XXX_Size() int {
	return xxx_messageInfo_TablesDatasetMetadata.Size(m)
}
func (m *TablesDatasetMetadata) XXX_DiscardUnknown() {
	xxx_messageInfo_TablesDatasetMetadata.DiscardUnknown(m)
}

var xxx_messageInfo_TablesDatasetMetadata proto.InternalMessageInfo

func (m *TablesDatasetMetadata) GetPrimaryTableSpecId() string {
	if m != nil {
		return m.PrimaryTableSpecId
	}
	return ""
}

func (m *TablesDatasetMetadata) GetTargetColumnSpecId() string {
	if m != nil {
		return m.TargetColumnSpecId
	}
	return ""
}

func (m *TablesDatasetMetadata) GetWeightColumnSpecId() string {
	if m != nil {
		return m.WeightColumnSpecId
	}
	return ""
}

func (m *TablesDatasetMetadata) GetMlUseColumnSpecId() string {
	if m != nil {
		return m.MlUseColumnSpecId
	}
	return ""
}

func (m *TablesDatasetMetadata) GetTargetColumnCorrelations() map[string]*CorrelationStats {
	if m != nil {
		return m.TargetColumnCorrelations
	}
	return nil
}

func (m *TablesDatasetMetadata) GetStatsUpdateTime() *timestamp.Timestamp {
	if m != nil {
		return m.StatsUpdateTime
	}
	return nil
}

// Model metadata specific to AutoML Tables.
type TablesModelMetadata struct {
	// Additional optimization objective configuration. Required for
	// `MAXIMIZE_PRECISION_AT_RECALL` and `MAXIMIZE_RECALL_AT_PRECISION`,
	// otherwise unused.
	//
	// Types that are valid to be assigned 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"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *TablesModelMetadata) Reset()         { *m = TablesModelMetadata{} }
func (m *TablesModelMetadata) String() string { return proto.CompactTextString(m) }
func (*TablesModelMetadata) ProtoMessage()    {}
func (*TablesModelMetadata) Descriptor() ([]byte, []int) {
	return fileDescriptor_a4edb25480826735, []int{1}
}

func (m *TablesModelMetadata) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_TablesModelMetadata.Unmarshal(m, b)
}
func (m *TablesModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_TablesModelMetadata.Marshal(b, m, deterministic)
}
func (m *TablesModelMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TablesModelMetadata.Merge(m, src)
}
func (m *TablesModelMetadata) XXX_Size() int {
	return xxx_messageInfo_TablesModelMetadata.Size(m)
}
func (m *TablesModelMetadata) XXX_DiscardUnknown() {
	xxx_messageInfo_TablesModelMetadata.DiscardUnknown(m)
}

var xxx_messageInfo_TablesModelMetadata proto.InternalMessageInfo

type isTablesModelMetadata_AdditionalOptimizationObjectiveConfig interface {
	isTablesModelMetadata_AdditionalOptimizationObjectiveConfig()
}

type TablesModelMetadata_OptimizationObjectiveRecallValue struct {
	OptimizationObjectiveRecallValue float32 `protobuf:"fixed32,17,opt,name=optimization_objective_recall_value,json=optimizationObjectiveRecallValue,proto3,oneof"`
}

type TablesModelMetadata_OptimizationObjectivePrecisionValue struct {
	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() {
}

func (m *TablesModelMetadata) GetAdditionalOptimizationObjectiveConfig() isTablesModelMetadata_AdditionalOptimizationObjectiveConfig {
	if m != nil {
		return m.AdditionalOptimizationObjectiveConfig
	}
	return nil
}

func (m *TablesModelMetadata) GetOptimizationObjectiveRecallValue() float32 {
	if x, ok := m.GetAdditionalOptimizationObjectiveConfig().(*TablesModelMetadata_OptimizationObjectiveRecallValue); ok {
		return x.OptimizationObjectiveRecallValue
	}
	return 0
}

func (m *TablesModelMetadata) GetOptimizationObjectivePrecisionValue() float32 {
	if x, ok := m.GetAdditionalOptimizationObjectiveConfig().(*TablesModelMetadata_OptimizationObjectivePrecisionValue); ok {
		return x.OptimizationObjectivePrecisionValue
	}
	return 0
}

func (m *TablesModelMetadata) GetTargetColumnSpec() *ColumnSpec {
	if m != nil {
		return m.TargetColumnSpec
	}
	return nil
}

func (m *TablesModelMetadata) GetInputFeatureColumnSpecs() []*ColumnSpec {
	if m != nil {
		return m.InputFeatureColumnSpecs
	}
	return nil
}

func (m *TablesModelMetadata) GetOptimizationObjective() string {
	if m != nil {
		return m.OptimizationObjective
	}
	return ""
}

func (m *TablesModelMetadata) GetTablesModelColumnInfo() []*TablesModelColumnInfo {
	if m != nil {
		return m.TablesModelColumnInfo
	}
	return nil
}

func (m *TablesModelMetadata) GetTrainBudgetMilliNodeHours() int64 {
	if m != nil {
		return m.TrainBudgetMilliNodeHours
	}
	return 0
}

func (m *TablesModelMetadata) GetTrainCostMilliNodeHours() int64 {
	if m != nil {
		return m.TrainCostMilliNodeHours
	}
	return 0
}

func (m *TablesModelMetadata) GetDisableEarlyStopping() bool {
	if m != nil {
		return m.DisableEarlyStopping
	}
	return false
}

// XXX_OneofWrappers is for the internal use of the proto package.
func (*TablesModelMetadata) XXX_OneofWrappers() []interface{} {
	return []interface{}{
		(*TablesModelMetadata_OptimizationObjectiveRecallValue)(nil),
		(*TablesModelMetadata_OptimizationObjectivePrecisionValue)(nil),
	}
}

// Contains annotation details specific to Tables.
type TablesAnnotation struct {
	// 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"`
	XXX_NoUnkeyedLiteral  struct{}                 `json:"-"`
	XXX_unrecognized      []byte                   `json:"-"`
	XXX_sizecache         int32                    `json:"-"`
}

func (m *TablesAnnotation) Reset()         { *m = TablesAnnotation{} }
func (m *TablesAnnotation) String() string { return proto.CompactTextString(m) }
func (*TablesAnnotation) ProtoMessage()    {}
func (*TablesAnnotation) Descriptor() ([]byte, []int) {
	return fileDescriptor_a4edb25480826735, []int{2}
}

func (m *TablesAnnotation) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_TablesAnnotation.Unmarshal(m, b)
}
func (m *TablesAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_TablesAnnotation.Marshal(b, m, deterministic)
}
func (m *TablesAnnotation) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TablesAnnotation.Merge(m, src)
}
func (m *TablesAnnotation) XXX_Size() int {
	return xxx_messageInfo_TablesAnnotation.Size(m)
}
func (m *TablesAnnotation) XXX_DiscardUnknown() {
	xxx_messageInfo_TablesAnnotation.DiscardUnknown(m)
}

var xxx_messageInfo_TablesAnnotation proto.InternalMessageInfo

func (m *TablesAnnotation) GetScore() float32 {
	if m != nil {
		return m.Score
	}
	return 0
}

func (m *TablesAnnotation) GetPredictionInterval() *DoubleRange {
	if m != nil {
		return m.PredictionInterval
	}
	return nil
}

func (m *TablesAnnotation) GetValue() *_struct.Value {
	if m != nil {
		return m.Value
	}
	return nil
}

func (m *TablesAnnotation) GetTablesModelColumnInfo() []*TablesModelColumnInfo {
	if m != nil {
		return m.TablesModelColumnInfo
	}
	return nil
}

// An information specific to given column and Tables Model, in context
// of the Model and the predictions created by it.
type TablesModelColumnInfo struct {
	// Output only. The name of the ColumnSpec describing the column. Not
	// populated when this proto is outputted to BigQuery.
	ColumnSpecName string `protobuf:"bytes,1,opt,name=column_spec_name,json=columnSpecName,proto3" json:"column_spec_name,omitempty"`
	// Output only. The display name of the column (same as the display_name of
	// its ColumnSpec).
	ColumnDisplayName string `protobuf:"bytes,2,opt,name=column_display_name,json=columnDisplayName,proto3" json:"column_display_name,omitempty"`
	// Output only. When given as part of a Model (always populated):
	// Measurement of how much model predictions correctness on the TEST data
	// depend on values in this column. A value between 0 and 1, higher means
	// higher influence. These values are normalized - for all input feature
	// columns of a given model they add to 1.
	//
	// When given back by Predict (populated iff
	// [feature_importance
	// param][google.cloud.automl.v1beta1.PredictRequest.params] is set) or Batch
	// Predict (populated iff
	// [feature_importance][google.cloud.automl.v1beta1.PredictRequest.params]
	// param is set):
	// Measurement of how impactful for the prediction returned for the given row
	// the value in this column was. Specifically, the feature importance
	// specifies the marginal contribution that the feature made to the prediction
	// score compared to the baseline score. These values are computed using the
	// Sampled Shapley method.
	FeatureImportance    float32  `protobuf:"fixed32,3,opt,name=feature_importance,json=featureImportance,proto3" json:"feature_importance,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *TablesModelColumnInfo) Reset()         { *m = TablesModelColumnInfo{} }
func (m *TablesModelColumnInfo) String() string { return proto.CompactTextString(m) }
func (*TablesModelColumnInfo) ProtoMessage()    {}
func (*TablesModelColumnInfo) Descriptor() ([]byte, []int) {
	return fileDescriptor_a4edb25480826735, []int{3}
}

func (m *TablesModelColumnInfo) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_TablesModelColumnInfo.Unmarshal(m, b)
}
func (m *TablesModelColumnInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_TablesModelColumnInfo.Marshal(b, m, deterministic)
}
func (m *TablesModelColumnInfo) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TablesModelColumnInfo.Merge(m, src)
}
func (m *TablesModelColumnInfo) XXX_Size() int {
	return xxx_messageInfo_TablesModelColumnInfo.Size(m)
}
func (m *TablesModelColumnInfo) XXX_DiscardUnknown() {
	xxx_messageInfo_TablesModelColumnInfo.DiscardUnknown(m)
}

var xxx_messageInfo_TablesModelColumnInfo proto.InternalMessageInfo

func (m *TablesModelColumnInfo) GetColumnSpecName() string {
	if m != nil {
		return m.ColumnSpecName
	}
	return ""
}

func (m *TablesModelColumnInfo) GetColumnDisplayName() string {
	if m != nil {
		return m.ColumnDisplayName
	}
	return ""
}

func (m *TablesModelColumnInfo) GetFeatureImportance() float32 {
	if m != nil {
		return m.FeatureImportance
	}
	return 0
}

func init() {
	proto.RegisterType((*TablesDatasetMetadata)(nil), "google.cloud.automl.v1beta1.TablesDatasetMetadata")
	proto.RegisterMapType((map[string]*CorrelationStats)(nil), "google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry")
	proto.RegisterType((*TablesModelMetadata)(nil), "google.cloud.automl.v1beta1.TablesModelMetadata")
	proto.RegisterType((*TablesAnnotation)(nil), "google.cloud.automl.v1beta1.TablesAnnotation")
	proto.RegisterType((*TablesModelColumnInfo)(nil), "google.cloud.automl.v1beta1.TablesModelColumnInfo")
}

func init() {
	proto.RegisterFile("google/cloud/automl/v1beta1/tables.proto", fileDescriptor_a4edb25480826735)
}

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}
