googleapis/google/cloud/bigquery/v2/model.proto

// Copyright 2019 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/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/protobuf/empty.proto";
import "google/protobuf/timestamp.proto";
import "google/protobuf/wrappers.proto";
import "google/api/annotations.proto";

option go_package = "google.golang.org/genproto/googleapis/cloud/bigquery/v2;bigquery";
option java_outer_classname = "ModelProto";
option java_package = "com.google.cloud.bigquery.v2";

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/bigquery.readonly,"
      "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.method_signature) = "project_id,dataset_id,model_id";
  }

  // Lists all models in the specified dataset. Requires the READER dataset
  // role.
  rpc ListModels(ListModelsRequest) returns (ListModelsResponse) {
    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.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.method_signature) = "project_id,dataset_id,model_id";
  }
}

message Model {
  message KmeansEnums {
    // Indicates the method used to initialize the centroids for KMeans
    // clustering algorithm.
    enum KmeansInitializationMethod {
      KMEANS_INITIALIZATION_METHOD_UNSPECIFIED = 0;

      // Initializes the centroids randomly.
      RANDOM = 1;

      // Initializes the centroids using data specified in
      // kmeans_initialization_column.
      CUSTOM = 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.
    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 classfication 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;

        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;

    // [Beta] Information for all clusters.
    repeated Cluster clusters = 3;
  }

  // Evaluation metrics of a model. These are either computed on all training
  // data or just the eval data based on whether eval data was used during
  // training. These are not present for imported models.
  message EvaluationMetrics {
    oneof metrics {
      // Populated for regression models and explicit feedback type matrix
      // factorization models.
      RegressionMetrics regression_metrics = 1;

      // Populated for binary classification/classifier models.
      BinaryClassificationMetrics binary_classification_metrics = 2;

      // Populated for multi-class classification/classifier models.
      MultiClassClassificationMetrics multi_class_classification_metrics = 3;

      // Populated for clustering models.
      ClusteringMetrics clustering_metrics = 4;
    }
  }

  // Information about a single training query run for the model.
  message TrainingRun {
    message TrainingOptions {
      // The maximum number of iterations in training. Used only for iterative
      // training algorithms.
      int64 max_iterations = 1;

      // Type of loss function used during training run.
      LossType loss_type = 2;

      // Learning rate in training. Used only for iterative training algorithms.
      double learn_rate = 3;

      // L1 regularization coefficient.
      google.protobuf.DoubleValue l1_regularization = 4;

      // L2 regularization coefficient.
      google.protobuf.DoubleValue l2_regularization = 5;

      // When early_stop is true, stops training when accuracy improvement is
      // less than 'min_relative_progress'. Used only for iterative training
      // algorithms.
      google.protobuf.DoubleValue min_relative_progress = 6;

      // Whether to train a model from the last checkpoint.
      google.protobuf.BoolValue warm_start = 7;

      // Whether to stop early when the loss doesn't improve significantly
      // any more (compared to min_relative_progress). Used only for iterative
      // training algorithms.
      google.protobuf.BoolValue early_stop = 8;

      // Name of input label columns in training data.
      repeated string input_label_columns = 9;

      // The data split type for training and evaluation, e.g. RANDOM.
      DataSplitMethod data_split_method = 10;

      // The fraction of evaluation data over the whole input data. The rest
      // of data will be used as training data. The format should be double.
      // Accurate to two decimal places.
      // Default value is 0.2.
      double data_split_eval_fraction = 11;

      // The column to split data with. This column won't be used as a
      // feature.
      // 1. When data_split_method is CUSTOM, the corresponding column should
      // be boolean. The rows with true value tag are eval data, and the false
      // are training data.
      // 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION
      // rows (from smallest to largest) in the corresponding column are used
      // as training data, and the rest are eval data. It respects the order
      // in Orderable data types:
      // https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
      string data_split_column = 12;

      // The strategy to determine learn rate for the current iteration.
      LearnRateStrategy learn_rate_strategy = 13;

      // Specifies the initial learning rate for the line search learn rate
      // strategy.
      double initial_learn_rate = 16;

      // Weights associated with each label class, for rebalancing the
      // training data. Only applicable for classification models.
      map<string, double> label_class_weights = 17;

      // Distance type for clustering models.
      DistanceType distance_type = 20;

      // Number of clusters for clustering models.
      int64 num_clusters = 21;

      // [Beta] Google Cloud Storage URI from which the model was imported. Only
      // applicable for imported models.
      string model_uri = 22;

      // Optimization strategy for training linear regression models.
      OptimizationStrategy optimization_strategy = 23;

      // The method used to initialize the centroids for kmeans algorithm.
      KmeansEnums.KmeansInitializationMethod kmeans_initialization_method = 33;

      // The column used to provide the initial centroids for kmeans algorithm
      // when kmeans_initialization_method is CUSTOM.
      string kmeans_initialization_column = 34;
    }

    // Information about a single iteration of the training run.
    message IterationResult {
      // Information about a single cluster for clustering model.
      message ClusterInfo {
        // Centroid id.
        int64 centroid_id = 1;

        // Cluster radius, the average distance from centroid
        // to each point assigned to the cluster.
        google.protobuf.DoubleValue cluster_radius = 2;

        // Cluster size, the total number of points assigned to the cluster.
        google.protobuf.Int64Value cluster_size = 3;
      }

      // Index of the iteration, 0 based.
      google.protobuf.Int32Value index = 1;

      // Time taken to run the iteration in milliseconds.
      google.protobuf.Int64Value duration_ms = 4;

      // Loss computed on the training data at the end of iteration.
      google.protobuf.DoubleValue training_loss = 5;

      // Loss computed on the eval data at the end of iteration.
      google.protobuf.DoubleValue eval_loss = 6;

      // Learn rate used for this iteration.
      double learn_rate = 7;

      // Information about top clusters for clustering models.
      repeated ClusterInfo cluster_infos = 8;
    }

    // Options that were used for this training run, includes
    // user specified and default options that were used.
    TrainingOptions training_options = 1;

    // The start time of this training run.
    google.protobuf.Timestamp start_time = 8;

    // Output of each iteration run, results.size() <= max_iterations.
    repeated IterationResult results = 6;

    // The evaluation metrics over training/eval data that were computed at the
    // end of training.
    EvaluationMetrics evaluation_metrics = 7;
  }

  // Indicates the type of the Model.
  enum ModelType {
    MODEL_TYPE_UNSPECIFIED = 0;

    // Linear regression model.
    LINEAR_REGRESSION = 1;

    // Logistic regression based classification model.
    LOGISTIC_REGRESSION = 2;

    // K-means clustering model.
    KMEANS = 3;

    // [Beta] An imported TensorFlow model.
    TENSORFLOW = 6;
  }

  // Loss metric to evaluate model training performance.
  enum LossType {
    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 {
    DISTANCE_TYPE_UNSPECIFIED = 0;

    // Eculidean distance.
    EUCLIDEAN = 1;

    // Cosine distance.
    COSINE = 2;
  }

  // Indicates the method to split input data into multiple tables.
  enum DataSplitMethod {
    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;
  }

  // Indicates the learning rate optimization strategy to use.
  enum LearnRateStrategy {
    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 {
    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;
  }

  // Output only. A hash of this resource.
  string etag = 1 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Required. Unique identifier for this model.
  ModelReference model_reference = 2 [(google.api.field_behavior) = REQUIRED];

  // Output only. The time when this model was created, in millisecs since the epoch.
  int64 creation_time = 5 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. The time when this model was last modified, in millisecs since the epoch.
  int64 last_modified_time = 6 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Optional. A user-friendly description of this model.
  string description = 12 [(google.api.field_behavior) = OPTIONAL];

  // Optional. A descriptive name for this model.
  string friendly_name = 14 [(google.api.field_behavior) = OPTIONAL];

  // The labels associated with this model. You can use these to organize
  // and group your models. Label keys and values can be no longer
  // than 63 characters, can only contain lowercase letters, numeric
  // characters, underscores and dashes. International characters are allowed.
  // Label values are optional. Label keys must start with a letter and each
  // label in the list must have a different key.
  map<string, string> labels = 15;

  // Optional. The time when this model expires, in milliseconds since the epoch.
  // If not present, the model will persist indefinitely. Expired models
  // will be deleted and their storage reclaimed.  The defaultTableExpirationMs
  // property of the encapsulating dataset can be used to set a default
  // expirationTime on newly created models.
  int64 expiration_time = 16 [(google.api.field_behavior) = OPTIONAL];

  // Output only. The geographic location where the model resides. This value
  // is inherited from the dataset.
  string location = 13 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Custom encryption configuration (e.g., Cloud KMS keys). This shows the
  // encryption configuration of the model data while stored in BigQuery
  // storage.
  google.cloud.bigquery.v2.EncryptionConfiguration encryption_configuration = 17;

  // Output only. Type of the model resource.
  ModelType model_type = 7 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Information for all training runs in increasing order of start_time.
  repeated TrainingRun training_runs = 9 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Input feature columns that were used to train this model.
  repeated StandardSqlField feature_columns = 10 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Label columns that were used to train this model.
  // The output of the model will have a "predicted_" prefix to these columns.
  repeated StandardSqlField label_columns = 11 [(google.api.field_behavior) = OUTPUT_ONLY];
}

message GetModelRequest {
  // Required. Project ID of the requested model.
  string project_id = 1 [(google.api.field_behavior) = REQUIRED];

  // Required. Dataset ID of the requested model.
  string dataset_id = 2 [(google.api.field_behavior) = REQUIRED];

  // Required. Model ID of the requested model.
  string model_id = 3 [(google.api.field_behavior) = REQUIRED];
}

message PatchModelRequest {
  // Required. Project ID of the model to patch.
  string project_id = 1 [(google.api.field_behavior) = REQUIRED];

  // Required. Dataset ID of the model to patch.
  string dataset_id = 2 [(google.api.field_behavior) = REQUIRED];

  // Required. Model ID of the model to patch.
  string model_id = 3 [(google.api.field_behavior) = REQUIRED];

  // Required. Patched model.
  // Follows RFC5789 patch semantics. Missing fields are not updated.
  // To clear a field, explicitly set to default value.
  Model model = 4 [(google.api.field_behavior) = REQUIRED];
}

message DeleteModelRequest {
  // Required. Project ID of the model to delete.
  string project_id = 1 [(google.api.field_behavior) = REQUIRED];

  // Required. Dataset ID of the model to delete.
  string dataset_id = 2 [(google.api.field_behavior) = REQUIRED];

  // Required. Model ID of the model to delete.
  string model_id = 3 [(google.api.field_behavior) = REQUIRED];
}

message ListModelsRequest {
  // Required. Project ID of the models to list.
  string project_id = 1 [(google.api.field_behavior) = REQUIRED];

  // Required. Dataset ID of the models to list.
  string dataset_id = 2 [(google.api.field_behavior) = REQUIRED];

  // The maximum number of results to return in a single response page.
  // Leverage the page tokens to iterate through the entire collection.
  google.protobuf.UInt32Value max_results = 3;

  // Page token, returned by a previous call to request the next page of
  // results
  string page_token = 4;
}

message ListModelsResponse {
  // Models in the requested dataset. Only the following fields are populated:
  // model_reference, model_type, creation_time, last_modified_time and
  // labels.
  repeated Model models = 1;

  // A token to request the next page of results.
  string next_page_token = 2;
}