// Copyright 2019 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

syntax = "proto2";
option optimize_for = LITE_RUNTIME;
option java_package = "org.chromium.components.optimization_guide.proto";
option java_outer_classname = "ModelsProto";

package optimization_guide.proto;

import "common_types.proto";

// A generic handle for any type of model.
message Model {
  reserved 3, 4;

  oneof model {
    DecisionTree decision_tree = 1;
    Ensemble ensemble = 2;
  }
  // The tag number is high to allow models to be added and an uncommon number
  // in case the proto this is generated from adds a similar functionality.
  optional DoubleValue threshold = 123;
}

// An ensemble prediction model consisting of an ordered sequence of models.
// This message can be used to express bagged or boosted models.
message Ensemble {
  reserved 2, 3, 4;

  message Member { optional Model submodel = 1; }
  // The tag number is set by the proto this is generated from and cannot be
  // changed.
  repeated Member members = 100;
}

// A decision tree model with its weight for use if included in an ensemble.
message DecisionTree {
  reserved 2;

  repeated TreeNode nodes = 1;
  optional float weight = 3;
}

// A node of a decision tree that is a binary deicison or a leaf.
message TreeNode {
  reserved 6, 7;

  // Following fields are provided for convenience and better readability.
  // Filling them in is not required.
  optional Int32Value node_id = 1;
  optional Int32Value depth = 2;
  optional Int32Value subtree_size = 3;

  oneof node_type {
    BinaryNode binary_node = 4;
    Leaf leaf = 5;
  }
}

// A tree node that contains an inequality test that during evaluation
// determines whether to continue the left or right child.
message BinaryNode {
  reserved 3, 5;

  optional Int32Value left_child_id = 1;
  optional Int32Value right_child_id = 2;
  enum Direction {
    LEFT = 0;
    RIGHT = 1;
  }
  // When a datapoint satisfies the test, it should be propagated to the left
  // child.
  optional InequalityTest inequality_left_child_test = 4;
}

// Vector of values for use within Models.
message Vector {
  repeated Value value = 1;
}

// A leaf node of a decision tree.
message Leaf {
  reserved 2, 3;

  optional Vector vector = 1;
}

// The ID for the features used during evaluation of a Model.
message FeatureId {
  reserved 2;

  optional StringValue id = 1;
}

// The set of inequality operations supported by binary nodes for
// decision tree models.
message InequalityTest {
  reserved 4;

  // When the feature is missing, the test's outcome is undefined.
  optional FeatureId feature_id = 1;
  enum Type {
    LESS_OR_EQUAL = 0;
    LESS_THAN = 1;
    GREATER_OR_EQUAL = 2;
    GREATER_THAN = 3;
  };
  optional Type type = 2;
  optional Value threshold = 3;
}

// Represents a single value of any type, e.g. 5 or "abc".
message Value {
  reserved 5;

  oneof value {
    float float_value = 1;
    double double_value = 2;
    int32 int32_value = 3;
    int64 int64_value = 4;
  }
}

// Wrapper message for `int32`.
//
// The JSON representation for `Int32Value` is JSON number.
message Int32Value {
  // The int32 value.
  optional int32 value = 1;
}

// Wrapper message for `string`.
//
// The JSON representation for `StringValue` is JSON string.
message StringValue {
  // The string value.
  optional string value = 1;
}

// Wrapper message for `double`.
//
// The JSON representation for `DoubleValue` is JSON number.
message DoubleValue {
  // The double value.
  optional double value = 1;
}

// Requests prediction models to be used for a set of optimization targets.
message GetModelsRequest {
  // Information about the requested models.
  repeated ModelInfo requested_models = 1;
  // The set of hosts to get additional metadata for, if applicable.
  repeated string hosts = 2;
  // Context in which this request is made.
  //
  // If the context matches one that requires more urgency (i.e.
  // CONTEXT_PAGE_NAVIGATION), then no model updates will be returned for the
  // requested models.
  optional RequestContext request_context = 3;
}

// Response to the GetModels request.
message GetModelsResponse {
  // The models to be used during prediction for the requested optimization
  // targets.
  repeated PredictionModel models = 1;
  // A set of model features and their values for the hosts contained in the
  // request to be expected to be consulted with during prediction.
  //
  // It is not guaranteed that this set will contain an entry for every
  // requested host.
  repeated HostModelFeatures host_model_features = 2;
}

// Holds the prediction model for a particular optimization target.
message PredictionModel {
  // Information about the model.
  optional ModelInfo model_info = 1;
  // The model to evaluate for the attached model information.
  //
  // This will only be set if the model that the client claims it has is stale.
  // It is also guaranteed that the value populated as part of this field is one
  // that the client claims to support based on the request's client model
  // capabilities.
  optional Model model = 2;
}

//  Metadata for a prediction model for a specific optimization target.
message ModelInfo {
  // The optimization target for which the model predicts.
  optional OptimizationTarget optimization_target = 1;
  // The version of the model, which is specific to the optimization target.
  optional int64 version = 2;
  // The set of model features that are supported by the model.
  //
  // If in the request, this represents the set of features that the client
  // understands how to evaluate. If in the response, this represents the set
  // of features referenced by the model.
  repeated ClientModelFeature supported_model_features = 3;
  // The set of model types the requesting client can use to make predictions.
  repeated ModelType supported_model_types = 4;
  // The set of host model features that are referenced by the model.
  //
  // Note that this should only be populated if part of the response.
  repeated string supported_host_model_features = 5;
}

// The scenarios for which the optimization guide has models for.
enum OptimizationTarget {
  OPTIMIZATION_TARGET_UNKNOWN = 0;
  // Should only be applied when the page load is predicted to be painful.
  OPTIMIZATION_TARGET_PAINFUL_PAGE_LOAD = 1;
}

// The features that only the client can compute during prediction and also
// knows how to evaluate.
enum ClientModelFeature {
  CLIENT_MODEL_FEATURE_UNKNOWN = 0;
  // The effective connection type for the page load.
  CLIENT_MODEL_FEATURE_EFFECTIVE_CONNECTION_TYPE = 1;
  // How the current page load transitioned from the previous one.
  CLIENT_MODEL_FEATURE_PAGE_TRANSITION = 2;
  // The site engagement score of the main frame host for the page load.
  CLIENT_MODEL_FEATURE_SITE_ENGAGEMENT_SCORE = 3;
  // Whether the origin for the page load matches the origin of the previous
  // page load.
  CLIENT_MODEL_FEATURE_SAME_ORIGIN_NAVIGATION = 4;
  // The mean of the duration from navigation to first contentful paint for the
  // session.
  CLIENT_MODEL_FEATURE_FIRST_CONTENTFUL_PAINT_SESSION_MEAN = 5;
  // The standard deviation of the duration from navigation to first
  // contentful paint for the session.
  CLIENT_MODEL_FEATURE_FIRST_CONTENTFUL_PAINT_SESSION_STANDARD_DEVIATION = 6;
  // The duration from navigation to first contentful paint for the previous
  // page load, if applicable.
  CLIENT_MODEL_FEATURE_FIRST_CONTENTFUL_PAINT_PREVIOUS_PAGE_LOAD = 7;
}

// The types of models that can be evaluated.
enum ModelType {
  MODEL_TYPE_UNKNOWN = 0;
  // A decision tree.
  MODEL_TYPE_DECISION_TREE = 1;
}

// A set of model features and the host that it applies to.
message HostModelFeatures {
  // The host that the features should be applied for.
  optional string host = 1;
  // The set of features and their values that apply to the host.
  repeated ModelFeature model_features = 2;
}

// Information about a feature that is potentially referenced in a model.
message ModelFeature {
  // The name of the feature to match if encountered in a model.
  optional string feature_name = 1;
  // The value of the feature to be used during prediction.
  oneof feature_value {
    double double_value = 2;
    int64 int64_value = 3;
  }
}