components/optimization_guide/proto/models.proto - codesearch/chromium/src - Git at Google

 // 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 "components/optimization_guide/proto/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 URL that the model can be downloaded from.
     string download_url = 5;
   }
   // 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 {
   reserved 2;

   // Information about the requested models.
   repeated ModelInfo requested_models = 1;
   // 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;
   // The field trials that are currently active when this request is made.
   repeated FieldTrial active_field_trials = 4;
   // The locale to associate with this request.
   //
   // It is the IETF language tag, defined in BCP 47. The region subtag is not
   // included when it adds no distinguishing information to the language tag
   // (e.g. both "en-US" and "fr" are correct here).
   optional string locale = 5;
 }

 // 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 {
   reserved 3;

   // 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 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;
   // Mechanism used for model owners to attach metadata to the request or
   // response.
   //
   // In practice, we expect this to be used as a way to negotiate capabilities.
   // The client can provide the model features they can evaluate if this field
   // is part of the request, and the server can provide the model features that
   // are actually present in the model.
   optional Any model_metadata = 6;
 }

 // 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;
   // Target for supplying the language detection model via the model downloader.
   OPTIMIZATION_TARGET_LANGUAGE_DETECTION = 2;
   // Target for determining topics present on a page.
   OPTIMIZATION_TARGET_PAGE_TOPICS = 3;
   // Target for segmentation: New tab page user.
   OPTIMIZATION_TARGET_SEGMENTATION_NEW_TAB = 4;
   // Target for segmentation: Share user.
   OPTIMIZATION_TARGET_SEGMENTATION_SHARE = 5;
   // Target for segmentation: Voice user.
   OPTIMIZATION_TARGET_SEGMENTATION_VOICE = 6;
 }

 // The types of models that can be evaluated.
 enum ModelType {
   MODEL_TYPE_UNKNOWN = 0;
   // A decision tree.
   MODEL_TYPE_DECISION_TREE = 1;
   // A model using only operations that are supported by TensorflowLite 2.3.0.
   MODEL_TYPE_TFLITE_2_3_0 = 2;
 }

 // 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;
   }
 }
	// 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 "components/optimization_guide/proto/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 URL that the model can be downloaded from.
	string download_url = 5;
	}
	// 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 {
	reserved 2;

	// Information about the requested models.
	repeated ModelInfo requested_models = 1;
	// 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;
	// The field trials that are currently active when this request is made.
	repeated FieldTrial active_field_trials = 4;
	// The locale to associate with this request.
	//
	// It is the IETF language tag, defined in BCP 47. The region subtag is not
	// included when it adds no distinguishing information to the language tag
	// (e.g. both "en-US" and "fr" are correct here).
	optional string locale = 5;
	}

	// 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 {
	reserved 3;

	// 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 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;
	// Mechanism used for model owners to attach metadata to the request or
	// response.
	//
	// In practice, we expect this to be used as a way to negotiate capabilities.
	// The client can provide the model features they can evaluate if this field
	// is part of the request, and the server can provide the model features that
	// are actually present in the model.
	optional Any model_metadata = 6;
	}

	// 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;
	// Target for supplying the language detection model via the model downloader.
	OPTIMIZATION_TARGET_LANGUAGE_DETECTION = 2;
	// Target for determining topics present on a page.
	OPTIMIZATION_TARGET_PAGE_TOPICS = 3;
	// Target for segmentation: New tab page user.
	OPTIMIZATION_TARGET_SEGMENTATION_NEW_TAB = 4;
	// Target for segmentation: Share user.
	OPTIMIZATION_TARGET_SEGMENTATION_SHARE = 5;
	// Target for segmentation: Voice user.
	OPTIMIZATION_TARGET_SEGMENTATION_VOICE = 6;
	}

	// The types of models that can be evaluated.
	enum ModelType {
	MODEL_TYPE_UNKNOWN = 0;
	// A decision tree.
	MODEL_TYPE_DECISION_TREE = 1;
	// A model using only operations that are supported by TensorflowLite 2.3.0.
	MODEL_TYPE_TFLITE_2_3_0 = 2;
	}

	// 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;
	}
	}