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

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

import proto "github.com/golang/protobuf/proto"
import fmt "fmt"
import math "math"
import _struct "github.com/golang/protobuf/ptypes/struct"
import timestamp "github.com/golang/protobuf/ptypes/timestamp"
import _ "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.ProtoPackageIsVersion2 // 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
	// * ARRAY(CATEGORY)
	// * FLOAT64
	// Furthermore, if the type is CATEGORY or ARRAY(CATEGORY), then only up to
	// 40 unique values may exist in that column across all rows, but for
	// ARRAY(CATEGORY) unique values are counted as elements of the ARRAY (i.e.
	// following 3 ARRAY-s: [A, B], [A], [B] are counted as having 2 unique
	// values).
	//
	// 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
	//
	// [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
	// and other columns of the
	//
	// [primary_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"`
	// 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_tables_61f65f5d47e1031c, []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 (dst *TablesDatasetMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TablesDatasetMetadata.Merge(dst, 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 {
	// 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.
	//
	// CLASSIFICATION_MULTI_CLASS :
	//   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
	//
	// CLASSIFICATION_MULTI_LABEL:
	//   "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).
	//
	// FORECASTING:
	//   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
	//   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
	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"`
	// 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.
	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"`
	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_tables_61f65f5d47e1031c, []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 (dst *TablesModelMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TablesModelMetadata.Merge(dst, 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

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
}

// 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 ARRAY(CATEGORY) data type, this is a confidence that one of the values
	// in the ARRAY would be the provided 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 (i.e. for regression predictions). 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 the above confidence `score`) FLOAT64 value.
	// ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
	//                   ARRAY in that column (with the above confidence `score`).
	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.
	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_tables_61f65f5d47e1031c, []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 (dst *TablesAnnotation) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TablesAnnotation.Merge(dst, 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:
	// 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 or Batch Predict:
	// Measurement of how impactful for the prediction returned for the given row
	// the value in this column was. A value between 0 and 1, higher means larger
	// impact. These values are normalized - for all input feature columns of a
	// single predicted row they add to 1.
	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_tables_61f65f5d47e1031c, []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 (dst *TablesModelColumnInfo) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TablesModelColumnInfo.Merge(dst, 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_tables_61f65f5d47e1031c)
}

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