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// Generic representation of tree-based models.
// This proto establishes a shared standard: "fully compatible" projects should
// provide support for all reasonable models expressed through it. Therefore,
// it should be kept as simple as possible, and should never contain
// project-specific design choices.
// Status: work in progress. This proto can change anytime without notice.
syntax = "proto3";
option cc_enable_arenas = true;
package tensorflow.decision_trees;
import "google/protobuf/any.proto";
import "google/protobuf/wrappers.proto";
// A generic handle for any type of model.
message Model {
oneof model {
DecisionTree decision_tree = 1;
Ensemble ensemble = 2;
google.protobuf.Any custom_model = 3;
}
repeated google.protobuf.Any additional_data = 4;
}
message ModelAndFeatures {
message Feature {
// TODO(jonasz): Remove this field, as it's confusing. Ctx: cr/153569450.
FeatureId feature_id = 1 [deprecated = true];
repeated google.protobuf.Any additional_data = 2;
};
// Given a FeatureId feature_id, the feature's description is in
// features[feature_id.id.value].
map<string, Feature> features = 1;
Model model = 2;
repeated google.protobuf.Any additional_data = 3;
}
// An ordered sequence of models. This message can be used to express bagged or
// boosted models, as well as custom ensembles.
message Ensemble {
message Member {
Model submodel = 1;
google.protobuf.Int32Value submodel_id = 2;
repeated google.protobuf.Any additional_data = 3;
}
repeated Member members = 100; // A higher id for more readable printing.
// The presence of a certain combination_technique indicates how to combine
// the outputs of member models in order to compute the ensemble's output.
oneof combination_technique {
Summation summation_combination_technique = 1;
Averaging averaging_combination_technique = 2;
google.protobuf.Any custom_combination_technique = 3;
}
repeated google.protobuf.Any additional_data = 4;
}
// When present, the Ensemble's output is the sum of member models' outputs.
message Summation {
repeated google.protobuf.Any additional_data = 1;
};
// When present, the Ensemble's output is the average of member models' outputs.
message Averaging {
repeated google.protobuf.Any additional_data = 1;
};
message DecisionTree {
repeated TreeNode nodes = 1;
repeated google.protobuf.Any additional_data = 2;
};
message TreeNode {
// Following fields are provided for convenience and better readability.
// Filling them in is not required.
google.protobuf.Int32Value node_id = 1;
google.protobuf.Int32Value depth = 2;
google.protobuf.Int32Value subtree_size = 3;
oneof node_type {
BinaryNode binary_node = 4;
Leaf leaf = 5;
google.protobuf.Any custom_node_type = 6;
}
repeated google.protobuf.Any additional_data = 7;
}
message BinaryNode {
google.protobuf.Int32Value left_child_id = 1;
google.protobuf.Int32Value right_child_id = 2;
enum Direction {
LEFT = 0;
RIGHT = 1;
}
// When left_child_test is undefined for a particular datapoint (e.g. because
// it's not defined when feature value is missing), the datapoint should go
// in this direction.
Direction default_direction = 3;
// When a datapoint satisfies the test, it should be propagated to the left
// child.
oneof left_child_test {
InequalityTest inequality_left_child_test = 4;
google.protobuf.Any custom_left_child_test = 5;
}
};
// A SparseVector represents a vector in which only certain select elements
// are non-zero. Maps labels to values (e.g. class id to probability or count).
message SparseVector {
map<int64, Value> sparse_value = 1;
}
message Vector {
repeated Value value = 1;
}
message Leaf {
oneof leaf {
// The interpretation of the values held in the leaves of a decision tree
// is application specific, but some common cases are:
// 1) len(vector) = 1, and the floating point value[0] holds the class 0
// probability in a two class classification problem.
// 2) len(vector) = 1, and the integer value[0] holds the class prediction.
// 3) The floating point value[i] holds the class i probability prediction.
// 4) The floating point value[i] holds the i-th component of the
// vector prediction in a regression problem.
// 5) sparse_vector holds the sparse class predictions for a classification
// problem with a large number of classes.
Vector vector = 1;
SparseVector sparse_vector = 2;
}
// For non-standard handling of leaves.
repeated google.protobuf.Any additional_data = 3;
};
message FeatureId {
google.protobuf.StringValue id = 1;
repeated google.protobuf.Any additional_data = 2;
};
message ObliqueFeatures {
// total value is sum(features[i] * weights[i]).
repeated FeatureId features = 1;
repeated float weights = 2;
}
message InequalityTest {
// When the feature is missing, the test's outcome is undefined.
oneof FeatureSum {
FeatureId feature_id = 1;
ObliqueFeatures oblique = 4;
}
enum Type {
LESS_OR_EQUAL = 0;
LESS_THAN = 1;
GREATER_OR_EQUAL = 2;
GREATER_THAN = 3;
};
Type type = 2;
Value threshold = 3;
};
// Represents a single value of any type, e.g. 5 or "abc".
message Value {
oneof value {
float float_value = 1;
double double_value = 2;
int32 int32_value = 3;
int64 int64_value = 4;
google.protobuf.Any custom_value = 5;
}
};
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