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syntax = "proto3";
option cc_enable_arenas = true;
package tensorflow.boosted_trees.learner;
// Tree regularization config.
message TreeRegularizationConfig {
// Classic L1/L2.
float l1 = 1;
float l2 = 2;
// Tree complexity penalizes overall model complexity effectively
// limiting how deep the tree can grow in regions with small gain.
float tree_complexity = 3;
}
// Tree constraints config.
message TreeConstraintsConfig {
// Maximum depth of the trees. The default value is 6 if not specified.
uint32 max_tree_depth = 1;
// Min hessian weight per node.
float min_node_weight = 2;
// Maximum number of unique features used in the tree. Zero means there is no
// limit.
int64 max_number_of_unique_feature_columns = 3;
}
// LearningRateConfig describes all supported learning rate tuners.
message LearningRateConfig {
oneof tuner {
LearningRateFixedConfig fixed = 1;
LearningRateDropoutDrivenConfig dropout = 2;
LearningRateLineSearchConfig line_search = 3;
}
}
// Config for a fixed learning rate.
message LearningRateFixedConfig {
float learning_rate = 1;
}
// Config for a tuned learning rate.
message LearningRateLineSearchConfig {
// Max learning rate. Must be strictly positive.
float max_learning_rate = 1;
// Number of learning rate values to consider between [0, max_learning_rate).
int32 num_steps = 2;
}
// When we have a sequence of trees 1, 2, 3 ... n, these essentially represent
// weights updates in functional space, and thus we can use averaging of weight
// updates to achieve better performance. For example, we can say that our final
// ensemble will be an average of ensembles of tree 1, and ensemble of tree 1
// and tree 2 etc .. ensemble of all trees.
// Note that this averaging will apply ONLY DURING PREDICTION. The training
// stays the same.
message AveragingConfig {
oneof config {
float average_last_n_trees = 1;
// Between 0 and 1. If set to 1.0, we are averaging ensembles of tree 1,
// ensemble of tree 1 and tree 2, etc ensemble of all trees. If set to 0.5,
// last half of the trees are averaged etc.
float average_last_percent_trees = 2;
}
}
message LearningRateDropoutDrivenConfig {
// Probability of dropping each tree in an existing so far ensemble.
float dropout_probability = 1;
// When trees are built after dropout happen, they don't "advance" to the
// optimal solution, they just rearrange the path. However you can still
// choose to skip dropout periodically, to allow a new tree that "advances"
// to be added.
// For example, if running for 200 steps with probability of dropout 1/100,
// you would expect the dropout to start happening for sure for all iterations
// after 100. However you can add probability_of_skipping_dropout of 0.1, this
// way iterations 100-200 will include approx 90 iterations of dropout and 10
// iterations of normal steps.Set it to 0 if you want just keep building
// the refinement trees after dropout kicks in.
float probability_of_skipping_dropout = 2;
// Between 0 and 1.
float learning_rate = 3;
}
message LearnerConfig {
enum PruningMode {
PRUNING_MODE_UNSPECIFIED = 0;
PRE_PRUNE = 1;
POST_PRUNE = 2;
}
enum GrowingMode {
GROWING_MODE_UNSPECIFIED = 0;
WHOLE_TREE = 1;
LAYER_BY_LAYER = 2;
}
enum MultiClassStrategy {
MULTI_CLASS_STRATEGY_UNSPECIFIED = 0;
TREE_PER_CLASS = 1;
FULL_HESSIAN = 2;
DIAGONAL_HESSIAN = 3;
}
// Number of classes.
uint32 num_classes = 1;
// Fraction of features to consider in each tree sampled randomly
// from all available features.
oneof feature_fraction {
float feature_fraction_per_tree = 2;
float feature_fraction_per_level = 3;
};
// Regularization.
TreeRegularizationConfig regularization = 4;
// Constraints.
TreeConstraintsConfig constraints = 5;
// Pruning. POST_PRUNE is the default pruning mode.
PruningMode pruning_mode = 8;
// Growing Mode. LAYER_BY_LAYER is the default growing mode.
GrowingMode growing_mode = 9;
// Learning rate. By default we use fixed learning rate of 0.1.
LearningRateConfig learning_rate_tuner = 6;
// Multi-class strategy. By default we use TREE_PER_CLASS for binary
// classification and linear regression. For other cases, we use
// DIAGONAL_HESSIAN as the default.
MultiClassStrategy multi_class_strategy = 10;
// If you want to average the ensembles (for regularization), provide the
// config below.
AveragingConfig averaging_config = 11;
}
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