org.tensorflow.framework.TreeMetadata Maven / Gradle / Ivy
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/kernels/boosted_trees/boosted_trees.proto
package org.tensorflow.framework;
/**
* Protobuf type {@code tensorflow.boosted_trees.TreeMetadata}
*/
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// @@protoc_insertion_point(message_implements:tensorflow.boosted_trees.TreeMetadata)
TreeMetadataOrBuilder {
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/**
* Protobuf type {@code tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate}
*/
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com.google.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate)
org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdateOrBuilder {
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// @@protoc_insertion_point(class_scope:tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate)
private static final org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate DEFAULT_INSTANCE;
static {
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public static org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate getDefaultInstance() {
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private int bitField0_;
public static final int NUM_LAYERS_GROWN_FIELD_NUMBER = 2;
private int numLayersGrown_;
/**
*
* Number of layers grown for this tree.
*
*
* int32 num_layers_grown = 2;
*/
public int getNumLayersGrown() {
return numLayersGrown_;
}
public static final int IS_FINALIZED_FIELD_NUMBER = 3;
private boolean isFinalized_;
/**
*
* Whether the tree is finalized in that no more layers can be grown.
*
*
* bool is_finalized = 3;
*/
public boolean getIsFinalized() {
return isFinalized_;
}
public static final int POST_PRUNED_NODES_META_FIELD_NUMBER = 4;
private java.util.List postPrunedNodesMeta_;
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public java.util.List getPostPrunedNodesMetaList() {
return postPrunedNodesMeta_;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public java.util.List
getPostPrunedNodesMetaOrBuilderList() {
return postPrunedNodesMeta_;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public int getPostPrunedNodesMetaCount() {
return postPrunedNodesMeta_.size();
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate getPostPrunedNodesMeta(int index) {
return postPrunedNodesMeta_.get(index);
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdateOrBuilder getPostPrunedNodesMetaOrBuilder(
int index) {
return postPrunedNodesMeta_.get(index);
}
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/**
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// @@protoc_insertion_point(builder_implements:tensorflow.boosted_trees.TreeMetadata)
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com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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private int bitField0_;
private int numLayersGrown_ ;
/**
*
* Number of layers grown for this tree.
*
*
* int32 num_layers_grown = 2;
*/
public int getNumLayersGrown() {
return numLayersGrown_;
}
/**
*
* Number of layers grown for this tree.
*
*
* int32 num_layers_grown = 2;
*/
public Builder setNumLayersGrown(int value) {
numLayersGrown_ = value;
onChanged();
return this;
}
/**
*
* Number of layers grown for this tree.
*
*
* int32 num_layers_grown = 2;
*/
public Builder clearNumLayersGrown() {
numLayersGrown_ = 0;
onChanged();
return this;
}
private boolean isFinalized_ ;
/**
*
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*
*
* bool is_finalized = 3;
*/
public boolean getIsFinalized() {
return isFinalized_;
}
/**
*
* Whether the tree is finalized in that no more layers can be grown.
*
*
* bool is_finalized = 3;
*/
public Builder setIsFinalized(boolean value) {
isFinalized_ = value;
onChanged();
return this;
}
/**
*
* Whether the tree is finalized in that no more layers can be grown.
*
*
* bool is_finalized = 3;
*/
public Builder clearIsFinalized() {
isFinalized_ = false;
onChanged();
return this;
}
private java.util.List postPrunedNodesMeta_ =
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private com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdateOrBuilder> postPrunedNodesMetaBuilder_;
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
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if (postPrunedNodesMetaBuilder_ == null) {
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/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
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if (postPrunedNodesMetaBuilder_ == null) {
return postPrunedNodesMeta_.size();
} else {
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}
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate getPostPrunedNodesMeta(int index) {
if (postPrunedNodesMetaBuilder_ == null) {
return postPrunedNodesMeta_.get(index);
} else {
return postPrunedNodesMetaBuilder_.getMessage(index);
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}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder setPostPrunedNodesMeta(
int index, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate value) {
if (postPrunedNodesMetaBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensurePostPrunedNodesMetaIsMutable();
postPrunedNodesMeta_.set(index, value);
onChanged();
} else {
postPrunedNodesMetaBuilder_.setMessage(index, value);
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return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder setPostPrunedNodesMeta(
int index, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder builderForValue) {
if (postPrunedNodesMetaBuilder_ == null) {
ensurePostPrunedNodesMetaIsMutable();
postPrunedNodesMeta_.set(index, builderForValue.build());
onChanged();
} else {
postPrunedNodesMetaBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder addPostPrunedNodesMeta(org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate value) {
if (postPrunedNodesMetaBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensurePostPrunedNodesMetaIsMutable();
postPrunedNodesMeta_.add(value);
onChanged();
} else {
postPrunedNodesMetaBuilder_.addMessage(value);
}
return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder addPostPrunedNodesMeta(
int index, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate value) {
if (postPrunedNodesMetaBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensurePostPrunedNodesMetaIsMutable();
postPrunedNodesMeta_.add(index, value);
onChanged();
} else {
postPrunedNodesMetaBuilder_.addMessage(index, value);
}
return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder addPostPrunedNodesMeta(
org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder builderForValue) {
if (postPrunedNodesMetaBuilder_ == null) {
ensurePostPrunedNodesMetaIsMutable();
postPrunedNodesMeta_.add(builderForValue.build());
onChanged();
} else {
postPrunedNodesMetaBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder addPostPrunedNodesMeta(
int index, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder builderForValue) {
if (postPrunedNodesMetaBuilder_ == null) {
ensurePostPrunedNodesMetaIsMutable();
postPrunedNodesMeta_.add(index, builderForValue.build());
onChanged();
} else {
postPrunedNodesMetaBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder addAllPostPrunedNodesMeta(
java.lang.Iterable values) {
if (postPrunedNodesMetaBuilder_ == null) {
ensurePostPrunedNodesMetaIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(
values, postPrunedNodesMeta_);
onChanged();
} else {
postPrunedNodesMetaBuilder_.addAllMessages(values);
}
return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder clearPostPrunedNodesMeta() {
if (postPrunedNodesMetaBuilder_ == null) {
postPrunedNodesMeta_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
} else {
postPrunedNodesMetaBuilder_.clear();
}
return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public Builder removePostPrunedNodesMeta(int index) {
if (postPrunedNodesMetaBuilder_ == null) {
ensurePostPrunedNodesMetaIsMutable();
postPrunedNodesMeta_.remove(index);
onChanged();
} else {
postPrunedNodesMetaBuilder_.remove(index);
}
return this;
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder getPostPrunedNodesMetaBuilder(
int index) {
return getPostPrunedNodesMetaFieldBuilder().getBuilder(index);
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdateOrBuilder getPostPrunedNodesMetaOrBuilder(
int index) {
if (postPrunedNodesMetaBuilder_ == null) {
return postPrunedNodesMeta_.get(index); } else {
return postPrunedNodesMetaBuilder_.getMessageOrBuilder(index);
}
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public java.util.List
getPostPrunedNodesMetaOrBuilderList() {
if (postPrunedNodesMetaBuilder_ != null) {
return postPrunedNodesMetaBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(postPrunedNodesMeta_);
}
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder addPostPrunedNodesMetaBuilder() {
return getPostPrunedNodesMetaFieldBuilder().addBuilder(
org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.getDefaultInstance());
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder addPostPrunedNodesMetaBuilder(
int index) {
return getPostPrunedNodesMetaFieldBuilder().addBuilder(
index, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.getDefaultInstance());
}
/**
*
* If tree was finalized and post pruning happened, it is possible that cache
* still refers to some nodes that were deleted or that the node ids changed
* (e.g. node id 5 became node id 2 due to pruning of the other branch).
* The mapping below allows us to understand where the old ids now map to and
* how the values should be adjusted due to post-pruning.
* The size of the list should be equal to the number of nodes in the tree
* before post-pruning happened.
* If the node was pruned, it will have new_node_id equal to the id of a node
* that this node was collapsed into. For a node that didn't get pruned, it is
* possible that its id still changed, so new_node_id will have the
* corresponding id in the pruned tree.
* If post-pruning didn't happen, or it did and it had no effect (e.g. no
* nodes got pruned), this list will be empty.
*
*
* repeated .tensorflow.boosted_trees.TreeMetadata.PostPruneNodeUpdate post_pruned_nodes_meta = 4;
*/
public java.util.List
getPostPrunedNodesMetaBuilderList() {
return getPostPrunedNodesMetaFieldBuilder().getBuilderList();
}
private com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdateOrBuilder>
getPostPrunedNodesMetaFieldBuilder() {
if (postPrunedNodesMetaBuilder_ == null) {
postPrunedNodesMetaBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdate.Builder, org.tensorflow.framework.TreeMetadata.PostPruneNodeUpdateOrBuilder>(
postPrunedNodesMeta_,
((bitField0_ & 0x00000004) == 0x00000004),
getParentForChildren(),
isClean());
postPrunedNodesMeta_ = null;
}
return postPrunedNodesMetaBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFieldsProto3(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.boosted_trees.TreeMetadata)
}
// @@protoc_insertion_point(class_scope:tensorflow.boosted_trees.TreeMetadata)
private static final org.tensorflow.framework.TreeMetadata DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.TreeMetadata();
}
public static org.tensorflow.framework.TreeMetadata getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
@java.lang.Override
public TreeMetadata parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return new TreeMetadata(input, extensionRegistry);
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public org.tensorflow.framework.TreeMetadata getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
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