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syntax = "proto3";
option cc_enable_arenas = true;
package tensorflow.boosted_trees.trees;
// TreeNode describes a node in a tree.
message TreeNode {
oneof node {
Leaf leaf = 1;
DenseFloatBinarySplit dense_float_binary_split = 2;
SparseFloatBinarySplitDefaultLeft sparse_float_binary_split_default_left =
3;
SparseFloatBinarySplitDefaultRight sparse_float_binary_split_default_right =
4;
CategoricalIdBinarySplit categorical_id_binary_split = 5;
CategoricalIdSetMembershipBinarySplit
categorical_id_set_membership_binary_split = 6;
}
TreeNodeMetadata node_metadata = 777;
}
// TreeNodeMetadata encodes metadata associated with each node in a tree.
message TreeNodeMetadata {
// The gain associated with this node.
float gain = 1;
// The original leaf node before this node was split.
Leaf original_leaf = 2;
}
// Leaves can either hold dense or sparse information.
message Leaf {
oneof leaf {
// See third_party/tensorflow/contrib/decision_trees/
// proto/generic_tree_model.proto
// for a description of how vector and sparse_vector might be used.
Vector vector = 1;
SparseVector sparse_vector = 2;
}
}
message Vector {
repeated float value = 1;
}
message SparseVector {
repeated int32 index = 1;
repeated float value = 2;
}
// Split rule for dense float features.
message DenseFloatBinarySplit {
// Float feature column and split threshold describing
// the rule feature <= threshold.
int32 feature_column = 1;
// If feature column is multivalent, this holds the index of the dimension
// for the split. Defaults to 0.
int32 dimension_id = 5;
float threshold = 2;
// Node children indexing into a contiguous
// vector of nodes starting from the root.
int32 left_id = 3;
int32 right_id = 4;
}
// Split rule for sparse float features defaulting left for missing features.
message SparseFloatBinarySplitDefaultLeft {
DenseFloatBinarySplit split = 1;
}
// Split rule for sparse float features defaulting right for missing features.
message SparseFloatBinarySplitDefaultRight {
DenseFloatBinarySplit split = 1;
}
// Split rule for categorical features with a single feature Id.
message CategoricalIdBinarySplit {
// Categorical feature column and Id describing
// the rule feature == Id.
int32 feature_column = 1;
int64 feature_id = 2;
// Node children indexing into a contiguous
// vector of nodes starting from the root.
int32 left_id = 3;
int32 right_id = 4;
}
// Split rule for categorical features with a set of feature Ids.
message CategoricalIdSetMembershipBinarySplit {
// Categorical feature column and Id describing
// the rule feature ∈ feature_ids.
int32 feature_column = 1;
// Sorted list of Ids in the set.
repeated int64 feature_ids = 2;
// Node children indexing into a contiguous
// vector of nodes starting from the root.
int32 left_id = 3;
int32 right_id = 4;
}
// DecisionTreeConfig describes a list of connected nodes.
// Node 0 must be the root and can carry any payload including a leaf
// in the case of representing the bias.
// Note that each node id is implicitly its index in the list of nodes.
message DecisionTreeConfig {
repeated TreeNode nodes = 1;
}
message DecisionTreeMetadata {
// How many times tree weight was updated (due to reweighting of the final
// ensemble, dropout, shrinkage etc).
int32 num_tree_weight_updates = 1;
// Number of layers grown for this tree.
int32 num_layers_grown = 2;
// Whether the tree is finalized in that no more layers can be grown.
bool is_finalized = 3;
}
message GrowingMetadata {
// Number of trees that we have attempted to build. After pruning, these
// trees might have been removed.
int64 num_trees_attempted = 1;
// Number of layers that we have attempted to build. After pruning, these
// layers might have been removed.
int64 num_layers_attempted = 2;
// Sorted list of column handlers that have been used in at least one split
// so far.
repeated int64 used_handler_ids = 3;
}
// DecisionTreeEnsembleConfig describes an ensemble of decision trees.
message DecisionTreeEnsembleConfig {
repeated DecisionTreeConfig trees = 1;
repeated float tree_weights = 2;
repeated DecisionTreeMetadata tree_metadata = 3;
// Metadata that is used during the training.
GrowingMetadata growing_metadata = 4;
}
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