// Copyright 2026 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.

syntax = "proto3";

package google.cloud.bigquery.v2;

import "google/api/annotations.proto";

import "google/api/client.proto";

import "google/api/field_behavior.proto";

import "google/cloud/bigquery/v2/encryption_config.proto";

import "google/cloud/bigquery/v2/model_reference.proto";

import "google/cloud/bigquery/v2/standard_sql.proto";

import "google/cloud/bigquery/v2/table_reference.proto";

import "google/protobuf/duration.proto";

import "google/protobuf/empty.proto";

import "google/protobuf/timestamp.proto";

import "google/protobuf/wrappers.proto";

option go_package = "cloud.google.com/go/bigquery/v2/apiv2/bigquerypb;bigquerypb";

option java_outer_classname = "ModelProto";

option java_package = "com.google.cloud.bigquery.v2";

// Model Service for BigQuery ML

service ModelService {

option (google.api.default_host) = "bigquery.googleapis.com";

option (google.api.oauth_scopes) =

"https://www.googleapis.com/auth/bigquery,"

"https://www.googleapis.com/auth/cloud-platform,"

"https://www.googleapis.com/auth/cloud-platform.read-only";

// Gets the specified model resource by model ID.

rpc GetModel(GetModelRequest) returns (Model) {

option (google.api.http) = {

get: "/bigquery/v2/projects/{project_id=*}/datasets/{dataset_id=*}/models/{model_id=*}"

};

option (google.api.method_signature) = "project_id,dataset_id,model_id";

}

// Lists all models in the specified dataset. Requires the READER dataset

// role. After retrieving the list of models, you can get information about a

// particular model by calling the models.get method.

rpc ListModels(ListModelsRequest) returns (ListModelsResponse) {

option (google.api.http) = {

get: "/bigquery/v2/projects/{project_id=*}/datasets/{dataset_id=*}/models"

};

option (google.api.method_signature) = "project_id,dataset_id,max_results";

}

// Patch specific fields in the specified model.

rpc PatchModel(PatchModelRequest) returns (Model) {

option (google.api.http) = {

patch: "/bigquery/v2/projects/{project_id=*}/datasets/{dataset_id=*}/models/{model_id=*}"

body: "model"

};

option (google.api.method_signature) =

"project_id,dataset_id,model_id,model";

}

// Deletes the model specified by modelId from the dataset.

rpc DeleteModel(DeleteModelRequest) returns (google.protobuf.Empty) {

option (google.api.http) = {

delete: "/bigquery/v2/projects/{project_id=*}/datasets/{dataset_id=*}/models/{model_id=*}"

};

option (google.api.method_signature) = "project_id,dataset_id,model_id";

}

}

// Remote Model Info

message RemoteModelInfo {

// Supported service type for remote model.

enum RemoteServiceType {

// Unspecified remote service type.

REMOTE_SERVICE_TYPE_UNSPECIFIED = 0;

// V3 Cloud AI Translation API. See more details at [Cloud Translation API]

// (https://cloud.google.com/translate/docs/reference/rest).

CLOUD_AI_TRANSLATE_V3 = 1;

// V1 Cloud AI Vision API See more details at [Cloud Vision API]

// (https://cloud.google.com/vision/docs/reference/rest).

CLOUD_AI_VISION_V1 = 2;

// V1 Cloud AI Natural Language API. See more details at [REST Resource:

// documents](https://cloud.google.com/natural-language/docs/reference/rest/v1/documents).

CLOUD_AI_NATURAL_LANGUAGE_V1 = 3;

// V2 Speech-to-Text API. See more details at [Google Cloud Speech-to-Text

// V2 API](https://cloud.google.com/speech-to-text/v2/docs)

CLOUD_AI_SPEECH_TO_TEXT_V2 = 7;

}

// Remote services are services outside of BigQuery used by remote models for

// predictions. A remote service is backed by either an arbitrary endpoint or

// a selected remote service type, but not both.

oneof remote_service {

// Output only. The endpoint for remote model.

string endpoint = 1 [(google.api.field_behavior) = OUTPUT_ONLY];

// Output only. The remote service type for remote model.

RemoteServiceType remote_service_type = 2

[(google.api.field_behavior) = OUTPUT_ONLY];

}

// Output only. Fully qualified name of the user-provided connection object of

// the remote model. Format:

// ```"projects/{project_id}/locations/{location_id}/connections/{connection_id}"```

string connection = 3 [(google.api.field_behavior) = OUTPUT_ONLY];

// Output only. Max number of rows in each batch sent to the remote service.

// If unset, the number of rows in each batch is set dynamically.

int64 max_batching_rows = 4 [(google.api.field_behavior) = OUTPUT_ONLY];

// Output only. The model version for LLM.

string remote_model_version = 5 [(google.api.field_behavior) = OUTPUT_ONLY];

// Output only. The name of the speech recognizer to use for speech

// recognition. The expected format is

// `projects/{project}/locations/{location}/recognizers/{recognizer}`.

// Customers can specify this field at model creation. If not specified, a

// default recognizer `projects/{model

// project}/locations/global/recognizers/_` will be used. See more details at

// [recognizers](https://cloud.google.com/speech-to-text/v2/docs/reference/rest/v2/projects.locations.recognizers)

string speech_recognizer = 7 [(google.api.field_behavior) = OUTPUT_ONLY];

}

// Information about a single transform column.

message TransformColumn {

// Output only. Name of the column.

string name = 1 [(google.api.field_behavior) = OUTPUT_ONLY];

// Output only. Data type of the column after the transform.

StandardSqlDataType type = 2 [(google.api.field_behavior) = OUTPUT_ONLY];

// Output only. The SQL expression used in the column transform.

string transform_sql = 3 [(google.api.field_behavior) = OUTPUT_ONLY];

}

message Model {

// Indicates the type of the Model.

enum ModelType {

// Default value.

MODEL_TYPE_UNSPECIFIED = 0;

// Linear regression model.

LINEAR_REGRESSION = 1;

// Logistic regression based classification model.

LOGISTIC_REGRESSION = 2;

// K-means clustering model.

KMEANS = 3;

// Matrix factorization model.

MATRIX_FACTORIZATION = 4;

// DNN classifier model.

DNN_CLASSIFIER = 5;

// An imported TensorFlow model.

TENSORFLOW = 6;

// DNN regressor model.

DNN_REGRESSOR = 7;

// An imported XGBoost model.

XGBOOST = 8;

// Boosted tree regressor model.

BOOSTED_TREE_REGRESSOR = 9;

// Boosted tree classifier model.

BOOSTED_TREE_CLASSIFIER = 10;

// ARIMA model.

ARIMA = 11;

// AutoML Tables regression model.

AUTOML_REGRESSOR = 12;

// AutoML Tables classification model.

AUTOML_CLASSIFIER = 13;

// Prinpical Component Analysis model.

PCA = 14;

// Wide-and-deep classifier model.

DNN_LINEAR_COMBINED_CLASSIFIER = 16;

// Wide-and-deep regressor model.

DNN_LINEAR_COMBINED_REGRESSOR = 17;

// Autoencoder model.

AUTOENCODER = 18;

// New name for the ARIMA model.

ARIMA_PLUS = 19;

// ARIMA with external regressors.

ARIMA_PLUS_XREG = 23;

// Random forest regressor model.

RANDOM_FOREST_REGRESSOR = 24;

// Random forest classifier model.

RANDOM_FOREST_CLASSIFIER = 25;

// An imported TensorFlow Lite model.

TENSORFLOW_LITE = 26;

// An imported ONNX model.

ONNX = 28;

// Model to capture the columns and logic in the TRANSFORM clause along with

// statistics useful for ML analytic functions.

TRANSFORM_ONLY = 29;

// The contribution analysis model.

CONTRIBUTION_ANALYSIS = 37;

}

// Loss metric to evaluate model training performance.

enum LossType {

// Default value.

LOSS_TYPE_UNSPECIFIED = 0;

// Mean squared loss, used for linear regression.

MEAN_SQUARED_LOSS = 1;

// Mean log loss, used for logistic regression.

MEAN_LOG_LOSS = 2;

}

// Distance metric used to compute the distance between two points.

enum DistanceType {

// Default value.

DISTANCE_TYPE_UNSPECIFIED = 0;

// Eculidean distance.

EUCLIDEAN = 1;

// Cosine distance.

COSINE = 2;

}

// Indicates the method to split input data into multiple tables.

enum DataSplitMethod {

// Default value.

DATA_SPLIT_METHOD_UNSPECIFIED = 0;

// Splits data randomly.

RANDOM = 1;

// Splits data with the user provided tags.

CUSTOM = 2;

// Splits data sequentially.

SEQUENTIAL = 3;

// Data split will be skipped.

NO_SPLIT = 4;

// Splits data automatically: Uses NO_SPLIT if the data size is small.

// Otherwise uses RANDOM.

AUTO_SPLIT = 5;

}

// Type of supported data frequency for time series forecasting models.

enum DataFrequency {

// Default value.

DATA_FREQUENCY_UNSPECIFIED = 0;

// Automatically inferred from timestamps.

AUTO_FREQUENCY = 1;

// Yearly data.

YEARLY = 2;

// Quarterly data.

QUARTERLY = 3;

// Monthly data.

MONTHLY = 4;

// Weekly data.

WEEKLY = 5;

// Daily data.

DAILY = 6;

// Hourly data.

HOURLY = 7;

// Per-minute data.

PER_MINUTE = 8;

}

// Type of supported holiday regions for time series forecasting models.

enum HolidayRegion {

// Holiday region unspecified.

HOLIDAY_REGION_UNSPECIFIED = 0;

// Global.

GLOBAL = 1;

// North America.

NA = 2;

// Japan and Asia Pacific: Korea, Greater China, India, Australia, and New

// Zealand.

JAPAC = 3;

// Europe, the Middle East and Africa.

EMEA = 4;

// Latin America and the Caribbean.

LAC = 5;

// United Arab Emirates

AE = 6;

// Argentina

AR = 7;

// Austria

AT = 8;

// Australia

AU = 9;

// Belgium

BE = 10;

// Brazil

BR = 11;

// Canada

CA = 12;

// Switzerland

CH = 13;

// Chile

CL = 14;

// China

CN = 15;

// Colombia

CO = 16;

// Czechoslovakia

CS = 17;

// Czech Republic

CZ = 18;

// Germany

DE = 19;

// Denmark

DK = 20;

// Algeria

DZ = 21;

// Ecuador

EC = 22;

// Estonia

EE = 23;

// Egypt

EG = 24;

// Spain

ES = 25;

// Finland

FI = 26;

// France

FR = 27;

// Great Britain (United Kingdom)

GB = 28;

// Greece

GR = 29;

// Hong Kong

HK = 30;

// Hungary

HU = 31;

// Indonesia

ID = 32;

// Ireland

IE = 33;

// Israel

IL = 34;

// India

IN = 35;

// Iran

IR = 36;

// Italy

IT = 37;

// Japan

JP = 38;

// Korea (South)

KR = 39;

// Latvia

LV = 40;

// Morocco

MA = 41;

// Mexico

MX = 42;

// Malaysia

MY = 43;

// Nigeria

NG = 44;

// Netherlands

NL = 45;

// Norway

NO = 46;

// New Zealand

NZ = 47;

// Peru

PE = 48;

// Philippines

PH = 49;

// Pakistan

PK = 50;

// Poland

PL = 51;

// Portugal

PT = 52;

// Romania

RO = 53;

// Serbia

RS = 54;

// Russian Federation

RU = 55;

// Saudi Arabia

SA = 56;

// Sweden

SE = 57;

// Singapore

SG = 58;

// Slovenia

SI = 59;

// Slovakia

SK = 60;

// Thailand

TH = 61;

// Turkey

TR = 62;

// Taiwan

TW = 63;

// Ukraine

UA = 64;

// United States

US = 65;

// Venezuela

VE = 66;

// Vietnam

VN = 67;

// South Africa

ZA = 68;

}

// Enums for seasonal period.

message SeasonalPeriod {

// Seasonal period type.

enum SeasonalPeriodType {

// Unspecified seasonal period.

SEASONAL_PERIOD_TYPE_UNSPECIFIED = 0;

// No seasonality

NO_SEASONALITY = 1;

// Daily period, 24 hours.

DAILY = 2;

// Weekly period, 7 days.

WEEKLY = 3;

// Monthly period, 30 days or irregular.

MONTHLY = 4;

// Quarterly period, 90 days or irregular.

QUARTERLY = 5;

// Yearly period, 365 days or irregular.

YEARLY = 6;

// Hourly period, 1 hour.

HOURLY = 7;

}

}

// Enums for color space, used for processing images in Object Table.

// See more details at

// https://www.tensorflow.org/io/tutorials/colorspace.

enum ColorSpace {

// Unspecified color space

COLOR_SPACE_UNSPECIFIED = 0;

// RGB

RGB = 1;

// HSV

HSV = 2;

// YIQ

YIQ = 3;

// YUV

YUV = 4;

// GRAYSCALE

GRAYSCALE = 5;

}

// Enums for kmeans model type.

message KmeansEnums {

// Indicates the method used to initialize the centroids for KMeans

// clustering algorithm.

enum KmeansInitializationMethod {

// Unspecified initialization method.

KMEANS_INITIALIZATION_METHOD_UNSPECIFIED = 0;

// Initializes the centroids randomly.

RANDOM = 1;

// Initializes the centroids using data specified in

// kmeans_initialization_column.

CUSTOM = 2;

// Initializes with kmeans++.

KMEANS_PLUS_PLUS = 3;

}

}

// Enums for XGBoost model type.

message BoostedTreeOptionEnums {

// Booster types supported. Refer to booster parameter in XGBoost.

enum BoosterType {

// Unspecified booster type.

BOOSTER_TYPE_UNSPECIFIED = 0;

// Gbtree booster.

GBTREE = 1;

// Dart booster.

DART = 2;

}

// Type of normalization algorithm for boosted tree models using dart

// booster. Refer to normalize_type in XGBoost.

enum DartNormalizeType {

// Unspecified dart normalize type.

DART_NORMALIZE_TYPE_UNSPECIFIED = 0;

// New trees have the same weight of each of dropped trees.

TREE = 1;

// New trees have the same weight of sum of dropped trees.

FOREST = 2;

}

// Tree construction algorithm used in boosted tree models.

// Refer to tree_method in XGBoost.

enum TreeMethod {

// Unspecified tree method.

TREE_METHOD_UNSPECIFIED = 0;

// Use heuristic to choose the fastest method.

AUTO = 1;

// Exact greedy algorithm.

EXACT = 2;

// Approximate greedy algorithm using quantile sketch and gradient

// histogram.

APPROX = 3;

// Fast histogram optimized approximate greedy algorithm.

HIST = 4;

}

}

// Enums for hyperparameter tuning.

message HparamTuningEnums {

// Available evaluation metrics used as hyperparameter tuning objectives.

enum HparamTuningObjective {

// Unspecified evaluation metric.

HPARAM_TUNING_OBJECTIVE_UNSPECIFIED = 0;

// Mean absolute error.

// mean_absolute_error = AVG(ABS(label - predicted))

MEAN_ABSOLUTE_ERROR = 1;

// Mean squared error.

// mean_squared_error = AVG(POW(label - predicted, 2))

MEAN_SQUARED_ERROR = 2;

// Mean squared log error.

// mean_squared_log_error = AVG(POW(LN(1 + label) - LN(1 + predicted), 2))

MEAN_SQUARED_LOG_ERROR = 3;

// Mean absolute error.

// median_absolute_error = APPROX_QUANTILES(absolute_error, 2)[OFFSET(1)]

MEDIAN_ABSOLUTE_ERROR = 4;

// R^2 score. This corresponds to r2_score in ML.EVALUATE.

// r_squared = 1 - SUM(squared_error)/(COUNT(label)*VAR_POP(label))

R_SQUARED = 5;

// Explained variance.

// explained_variance = 1 - VAR_POP(label_error)/VAR_POP(label)

EXPLAINED_VARIANCE = 6;

// 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 = 7;

// Recall is the fraction of actual positive labels that were given a

// positive prediction. For multiclass this is a macro-averaged metric.

RECALL = 8;

// Accuracy is the fraction of predictions given the correct label. For

// multiclass this is a globally micro-averaged metric.

ACCURACY = 9;

// The F1 score is an average of recall and precision. For multiclass this

// is a macro-averaged metric.

F1_SCORE = 10;

// Logarithmic Loss. For multiclass this is a macro-averaged metric.

LOG_LOSS = 11;

// Area Under an ROC Curve. For multiclass this is a macro-averaged

// metric.

ROC_AUC = 12;

// Davies-Bouldin Index.

DAVIES_BOULDIN_INDEX = 13;

// Mean Average Precision.

MEAN_AVERAGE_PRECISION = 14;

// Normalized Discounted Cumulative Gain.

NORMALIZED_DISCOUNTED_CUMULATIVE_GAIN = 15;

// Average Rank.

AVERAGE_RANK = 16;

}

}

// Indicates the learning rate optimization strategy to use.

enum LearnRateStrategy {

// Default value.

LEARN_RATE_STRATEGY_UNSPECIFIED = 0;

// Use line search to determine learning rate.

LINE_SEARCH = 1;

// Use a constant learning rate.

CONSTANT = 2;

}

// Indicates the optimization strategy used for training.

enum OptimizationStrategy {

// Default value.

OPTIMIZATION_STRATEGY_UNSPECIFIED = 0;

// Uses an iterative batch gradient descent algorithm.

BATCH_GRADIENT_DESCENT = 1;

// Uses a normal equation to solve linear regression problem.

NORMAL_EQUATION = 2;

}

// Indicates the training algorithm to use for matrix factorization models.

enum FeedbackType {

// Default value.

FEEDBACK_TYPE_UNSPECIFIED = 0;

// Use weighted-als for implicit feedback problems.

IMPLICIT = 1;

// Use nonweighted-als for explicit feedback problems.

EXPLICIT = 2;

}

// Evaluation metrics for regression and explicit feedback type matrix

// factorization models.

message RegressionMetrics {

// Mean absolute error.

google.protobuf.DoubleValue mean_absolute_error = 1;

// Mean squared error.

google.protobuf.DoubleValue mean_squared_error = 2;

// Mean squared log error.

google.protobuf.DoubleValue mean_squared_log_error = 3;

// Median absolute error.

google.protobuf.DoubleValue median_absolute_error = 4;

// R^2 score. This corresponds to r2_score in ML.EVALUATE.

google.protobuf.DoubleValue r_squared = 5;

}

// 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.

message AggregateClassificationMetrics {

// 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.

google.protobuf.DoubleValue precision = 1;

// Recall is the fraction of actual positive labels that were given a

// positive prediction. For multiclass this is a macro-averaged metric.

google.protobuf.DoubleValue recall = 2;

// Accuracy is the fraction of predictions given the correct label. For

// multiclass this is a micro-averaged metric.

google.protobuf.DoubleValue accuracy = 3;

// Threshold at which the metrics are computed. For binary

// classification models this is the positive class threshold.

// For multi-class classification models this is the confidence

// threshold.

google.protobuf.DoubleValue threshold = 4;

// The F1 score is an average of recall and precision. For multiclass

// this is a macro-averaged metric.

google.protobuf.DoubleValue f1_score = 5;

// Logarithmic Loss. For multiclass this is a macro-averaged metric.

google.protobuf.DoubleValue log_loss = 6;

// Area Under a ROC Curve. For multiclass this is a macro-averaged

// metric.

google.protobuf.DoubleValue roc_auc = 7;

}

// Evaluation metrics for binary classification/classifier models.

message BinaryClassificationMetrics {

// Confusion matrix for binary classification models.

message BinaryConfusionMatrix {

// Threshold value used when computing each of the following metric.

google.protobuf.DoubleValue positive_class_threshold = 1;

// Number of true samples predicted as true.

google.protobuf.Int64Value true_positives = 2;

// Number of false samples predicted as true.

google.protobuf.Int64Value false_positives = 3;

// Number of true samples predicted as false.

google.protobuf.Int64Value true_negatives = 4;

// Number of false samples predicted as false.

google.protobuf.Int64Value false_negatives = 5;

// The fraction of actual positive predictions that had positive actual

// labels.

google.protobuf.DoubleValue precision = 6;

// The fraction of actual positive labels that were given a positive

// prediction.

google.protobuf.DoubleValue recall = 7;

// The equally weighted average of recall and precision.

google.protobuf.DoubleValue f1_score = 8;

// The fraction of predictions given the correct label.

google.protobuf.DoubleValue accuracy = 9;

}

// Aggregate classification metrics.

AggregateClassificationMetrics aggregate_classification_metrics = 1;

// Binary confusion matrix at multiple thresholds.

repeated BinaryConfusionMatrix binary_confusion_matrix_list = 2;

// Label representing the positive class.

string positive_label = 3;

// Label representing the negative class.

string negative_label = 4;

}

// Evaluation metrics for multi-class classification/classifier models.

message MultiClassClassificationMetrics {

// Confusion matrix for multi-class classification models.

message ConfusionMatrix {

// A single entry in the confusion matrix.

message Entry {

// The predicted label. For confidence_threshold > 0, we will

// also add an entry indicating the number of items under the

// confidence threshold.

string predicted_label = 1;

// Number of items being predicted as this label.

google.protobuf.Int64Value item_count = 2;

}

// A single row in the confusion matrix.

message Row {

// The original label of this row.

string actual_label = 1;

// Info describing predicted label distribution.

repeated Entry entries = 2;

}

// Confidence threshold used when computing the entries of the

// confusion matrix.

google.protobuf.DoubleValue confidence_threshold = 1;

// One row per actual label.

repeated Row rows = 2;

}

// Aggregate classification metrics.

AggregateClassificationMetrics aggregate_classification_metrics = 1;

// Confusion matrix at different thresholds.

repeated ConfusionMatrix confusion_matrix_list = 2;

}

// Evaluation metrics for clustering models.

message ClusteringMetrics {

// Message containing the information about one cluster.

message Cluster {

// Representative value of a single feature within the cluster.

message FeatureValue {

// Representative value of a categorical feature.

message CategoricalValue {

// Represents the count of a single category within the cluster.

message CategoryCount {

// The name of category.

string category = 1;

// The count of training samples matching the category within the

// cluster.

google.protobuf.Int64Value count = 2;

}

// 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.

repeated CategoryCount category_counts = 1;

}

// The feature column name.

string feature_column = 1;

// Value.

oneof value {

// The numerical feature value. This is the centroid value for this

// feature.

google.protobuf.DoubleValue numerical_value = 2;

// The categorical feature value.

CategoricalValue categorical_value = 3;

}

}

// Centroid id.

int64 centroid_id = 1;

// Values of highly variant features for this cluster.

repeated FeatureValue feature_values = 2;

// Count of training data rows that were assigned to this cluster.

google.protobuf.Int64Value count = 3;

}

// Davies-Bouldin index.

google.protobuf.DoubleValue davies_bouldin_index = 1;

// Mean of squared distances between each sample to its cluster centroid.

google.protobuf.DoubleValue mean_squared_distance = 2;

// Information for all clusters.

repeated Cluster clusters = 3;

}

// Evaluation metrics used by weighted-ALS models specified by

// feedback_type=implicit.

message RankingMetrics {

// Calculates a precision per user for all the items by ranking them and

// then averages all the precisions across all the users.

google.protobuf.DoubleValue mean_average_precision = 1;

// Similar to the mean squared error computed in regression and explicit

// recommendation models except instead of computing the rating directly,

// the output from evaluate is computed against a preference which is 1 or 0

// depending on if the rating exists or not.

google.protobuf.DoubleValue mean_squared_error = 2;

// A metric to determine the goodness of a ranking calculated from the

// predicted confidence by comparing it to an ideal rank measured by the

// original ratings.

google.protobuf.DoubleValue normalized_discounted_cumulative_gain = 3;

// Determines the goodness of a ranking by computing the percentile rank

// from the predicted confidence and dividing it by the original rank.

google.protobuf.DoubleValue average_rank = 4;

}

// Model evaluation metrics for ARIMA forecasting models.

message ArimaForecastingMetrics {

// Model evaluation metrics for a single ARIMA forecasting model.

message ArimaSingleModelForecastingMetrics {

// Non-seasonal order.

ArimaOrder non_seasonal_order = 1;

// Arima fitting metrics.

ArimaFittingMetrics arima_fitting_metrics = 2;

// Is arima model fitted with drift or not. It is always false when d

// is not 1.

google.protobuf.BoolValue has_drift = 3;

// The time_series_id value for this time series. It will be one of

// the unique values from the time_series_id_column specified during

// ARIMA model training. Only present when time_series_id_column

// training option was used.

string time_series_id = 4;

// The tuple of time_series_ids identifying this time series. It will

// be one of the unique tuples of values present in the

// time_series_id_columns specified during ARIMA model training. Only

// present when time_series_id_columns training option was used and

// the order of values here are same as the order of

// time_series_id_columns.

repeated string time_series_ids = 9;

// Seasonal periods. Repeated because multiple periods are supported

// for one time series.