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xgboost/include/xgboost/tree_model.h
Rory Mitchell 13e7a2cff0 Various bug fixes (#2825) 
* Fatal error if GPU algorithm selected without GPU support compiled

* Resolve type conversion warnings

* Fix gpu unit test failure

* Fix compressed iterator edge case

* Fix python unit test failures due to flake8 update on pip
2017-10-25 14:45:01 +13:00

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/*!
* Copyright 2014 by Contributors
* \file tree_model.h
* \brief model structure for tree
* \author Tianqi Chen
*/
#ifndef XGBOOST_TREE_MODEL_H_
#define XGBOOST_TREE_MODEL_H_
#include <dmlc/io.h>
#include <dmlc/parameter.h>
#include <limits>
#include <vector>
#include <string>
#include <cstring>
#include <algorithm>
#include <tuple>
#include "./base.h"
#include "./data.h"
#include "./logging.h"
#include "./feature_map.h"
namespace xgboost {
/*! \brief meta parameters of the tree */
struct TreeParam : public dmlc::Parameter<TreeParam> {
/*! \brief number of start root */
int num_roots;
/*! \brief total number of nodes */
int num_nodes;
/*!\brief number of deleted nodes */
int num_deleted;
/*! \brief maximum depth, this is a statistics of the tree */
int max_depth;
/*! \brief number of features used for tree construction */
int num_feature;
/*!
* \brief leaf vector size, used for vector tree
* used to store more than one dimensional information in tree
*/
int size_leaf_vector;
/*! \brief reserved part, make sure alignment works for 64bit */
int reserved[31];
/*! \brief constructor */
TreeParam() {
// assert compact alignment
static_assert(sizeof(TreeParam) == (31 + 6) * sizeof(int),
"TreeParam: 64 bit align");
std::memset(this, 0, sizeof(TreeParam));
num_nodes = num_roots = 1;
}
// declare the parameters
DMLC_DECLARE_PARAMETER(TreeParam) {
// only declare the parameters that can be set by the user.
// other arguments are set by the algorithm.
DMLC_DECLARE_FIELD(num_roots).set_lower_bound(1).set_default(1)
.describe("Number of start root of trees.");
DMLC_DECLARE_FIELD(num_feature)
.describe("Number of features used in tree construction.");
DMLC_DECLARE_FIELD(size_leaf_vector).set_lower_bound(0).set_default(0)
.describe("Size of leaf vector, reserved for vector tree");
}
};
/*!
* \brief template class of TreeModel
* \tparam TSplitCond data type to indicate split condition
* \tparam TNodeStat auxiliary statistics of node to help tree building
*/
template<typename TSplitCond, typename TNodeStat>
class TreeModel {
public:
/*! \brief data type to indicate split condition */
typedef TNodeStat NodeStat;
/*! \brief auxiliary statistics of node to help tree building */
typedef TSplitCond SplitCond;
/*! \brief tree node */
class Node {
public:
Node() : sindex_(0) {
// assert compact alignment
static_assert(sizeof(Node) == 4 * sizeof(int) + sizeof(Info),
"Node: 64 bit align");
}
/*! \brief index of left child */
inline int cleft() const {
return this->cleft_;
}
/*! \brief index of right child */
inline int cright() const {
return this->cright_;
}
/*! \brief index of default child when feature is missing */
inline int cdefault() const {
return this->default_left() ? this->cleft() : this->cright();
}
/*! \brief feature index of split condition */
inline unsigned split_index() const {
return sindex_ & ((1U << 31) - 1U);
}
/*! \brief when feature is unknown, whether goes to left child */
inline bool default_left() const {
return (sindex_ >> 31) != 0;
}
/*! \brief whether current node is leaf node */
inline bool is_leaf() const {
return cleft_ == -1;
}
/*! \return get leaf value of leaf node */
inline bst_float leaf_value() const {
return (this->info_).leaf_value;
}
/*! \return get split condition of the node */
inline TSplitCond split_cond() const {
return (this->info_).split_cond;
}
/*! \brief get parent of the node */
inline int parent() const {
return parent_ & ((1U << 31) - 1);
}
/*! \brief whether current node is left child */
inline bool is_left_child() const {
return (parent_ & (1U << 31)) != 0;
}
/*! \brief whether this node is deleted */
inline bool is_deleted() const {
return sindex_ == std::numeric_limits<unsigned>::max();
}
/*! \brief whether current node is root */
inline bool is_root() const {
return parent_ == -1;
}
/*!
* \brief set the right child
* \param nid node id to right child
*/
inline void set_right_child(int nid) {
this->cright_ = nid;
}
/*!
* \brief set split condition of current node
* \param split_index feature index to split
* \param split_cond split condition
* \param default_left the default direction when feature is unknown
*/
inline void set_split(unsigned split_index, TSplitCond split_cond,
bool default_left = false) {
if (default_left) split_index |= (1U << 31);
this->sindex_ = split_index;
(this->info_).split_cond = split_cond;
}
/*!
* \brief set the leaf value of the node
* \param value leaf value
* \param right right index, could be used to store
* additional information
*/
inline void set_leaf(bst_float value, int right = -1) {
(this->info_).leaf_value = value;
this->cleft_ = -1;
this->cright_ = right;
}
/*! \brief mark that this node is deleted */
inline void mark_delete() {
this->sindex_ = std::numeric_limits<unsigned>::max();
}
private:
friend class TreeModel<TSplitCond, TNodeStat>;
/*!
* \brief in leaf node, we have weights, in non-leaf nodes,
* we have split condition
*/
union Info{
bst_float leaf_value;
TSplitCond split_cond;
};
// pointer to parent, highest bit is used to
// indicate whether it's a left child or not
int parent_;
// pointer to left, right
int cleft_, cright_;
// split feature index, left split or right split depends on the highest bit
unsigned sindex_;
// extra info
Info info_;
// set parent
inline void set_parent(int pidx, bool is_left_child = true) {
if (is_left_child) pidx |= (1U << 31);
this->parent_ = pidx;
}
};
protected:
// vector of nodes
std::vector<Node> nodes;
// free node space, used during training process
std::vector<int> deleted_nodes;
// stats of nodes
std::vector<TNodeStat> stats;
// leaf vector, that is used to store additional information
std::vector<bst_float> leaf_vector;
// allocate a new node,
// !!!!!! NOTE: may cause BUG here, nodes.resize
inline int AllocNode() {
if (param.num_deleted != 0) {
int nd = deleted_nodes.back();
deleted_nodes.pop_back();
--param.num_deleted;
return nd;
}
int nd = param.num_nodes++;
CHECK_LT(param.num_nodes, std::numeric_limits<int>::max())
<< "number of nodes in the tree exceed 2^31";
nodes.resize(param.num_nodes);
stats.resize(param.num_nodes);
leaf_vector.resize(param.num_nodes * param.size_leaf_vector);
return nd;
}
// delete a tree node, keep the parent field to allow trace back
inline void DeleteNode(int nid) {
CHECK_GE(nid, param.num_roots);
deleted_nodes.push_back(nid);
nodes[nid].mark_delete();
++param.num_deleted;
}
public:
/*!
* \brief change a non leaf node to a leaf node, delete its children
* \param rid node id of the node
* \param value new leaf value
*/
inline void ChangeToLeaf(int rid, bst_float value) {
CHECK(nodes[nodes[rid].cleft() ].is_leaf());
CHECK(nodes[nodes[rid].cright()].is_leaf());
this->DeleteNode(nodes[rid].cleft());
this->DeleteNode(nodes[rid].cright());
nodes[rid].set_leaf(value);
}
/*!
* \brief collapse a non leaf node to a leaf node, delete its children
* \param rid node id of the node
* \param value new leaf value
*/
inline void CollapseToLeaf(int rid, bst_float value) {
if (nodes[rid].is_leaf()) return;
if (!nodes[nodes[rid].cleft() ].is_leaf()) {
CollapseToLeaf(nodes[rid].cleft(), 0.0f);
}
if (!nodes[nodes[rid].cright() ].is_leaf()) {
CollapseToLeaf(nodes[rid].cright(), 0.0f);
}
this->ChangeToLeaf(rid, value);
}
public:
/*! \brief model parameter */
TreeParam param;
/*! \brief constructor */
TreeModel() {
param.num_nodes = 1;
param.num_roots = 1;
param.num_deleted = 0;
nodes.resize(1);
}
/*! \brief get node given nid */
inline Node& operator[](int nid) {
return nodes[nid];
}
/*! \brief get node given nid */
inline const Node& operator[](int nid) const {
return nodes[nid];
}
/*! \brief get const reference to nodes */
inline const std::vector<Node>& GetNodes() const { return nodes; }
/*! \brief get node statistics given nid */
inline NodeStat& stat(int nid) {
return stats[nid];
}
/*! \brief get node statistics given nid */
inline const NodeStat& stat(int nid) const {
return stats[nid];
}
/*! \brief get leaf vector given nid */
inline bst_float* leafvec(int nid) {
if (leaf_vector.size() == 0) return nullptr;
return& leaf_vector[nid * param.size_leaf_vector];
}
/*! \brief get leaf vector given nid */
inline const bst_float* leafvec(int nid) const {
if (leaf_vector.size() == 0) return nullptr;
return& leaf_vector[nid * param.size_leaf_vector];
}
/*! \brief initialize the model */
inline void InitModel() {
param.num_nodes = param.num_roots;
nodes.resize(param.num_nodes);
stats.resize(param.num_nodes);
leaf_vector.resize(param.num_nodes * param.size_leaf_vector, 0.0f);
for (int i = 0; i < param.num_nodes; i ++) {
nodes[i].set_leaf(0.0f);
nodes[i].set_parent(-1);
}
}
/*!
* \brief load model from stream
* \param fi input stream
*/
inline void Load(dmlc::Stream* fi) {
CHECK_EQ(fi->Read(&param, sizeof(TreeParam)), sizeof(TreeParam));
nodes.resize(param.num_nodes);
stats.resize(param.num_nodes);
CHECK_NE(param.num_nodes, 0);
CHECK_EQ(fi->Read(dmlc::BeginPtr(nodes), sizeof(Node) * nodes.size()),
sizeof(Node) * nodes.size());
CHECK_EQ(fi->Read(dmlc::BeginPtr(stats), sizeof(NodeStat) * stats.size()),
sizeof(NodeStat) * stats.size());
if (param.size_leaf_vector != 0) {
CHECK(fi->Read(&leaf_vector));
}
// chg deleted nodes
deleted_nodes.resize(0);
for (int i = param.num_roots; i < param.num_nodes; ++i) {
if (nodes[i].is_deleted()) deleted_nodes.push_back(i);
}
CHECK_EQ(static_cast<int>(deleted_nodes.size()), param.num_deleted);
}
/*!
* \brief save model to stream
* \param fo output stream
*/
inline void Save(dmlc::Stream* fo) const {
CHECK_EQ(param.num_nodes, static_cast<int>(nodes.size()));
CHECK_EQ(param.num_nodes, static_cast<int>(stats.size()));
fo->Write(&param, sizeof(TreeParam));
CHECK_NE(param.num_nodes, 0);
fo->Write(dmlc::BeginPtr(nodes), sizeof(Node) * nodes.size());
fo->Write(dmlc::BeginPtr(stats), sizeof(NodeStat) * nodes.size());
if (param.size_leaf_vector != 0) fo->Write(leaf_vector);
}
/*!
* \brief add child nodes to node
* \param nid node id to add children to
*/
inline void AddChilds(int nid) {
int pleft = this->AllocNode();
int pright = this->AllocNode();
nodes[nid].cleft_ = pleft;
nodes[nid].cright_ = pright;
nodes[nodes[nid].cleft() ].set_parent(nid, true);
nodes[nodes[nid].cright()].set_parent(nid, false);
}
/*!
* \brief only add a right child to a leaf node
* \param nid node id to add right child
*/
inline void AddRightChild(int nid) {
int pright = this->AllocNode();
nodes[nid].right = pright;
nodes[nodes[nid].right].set_parent(nid, false);
}
/*!
* \brief get current depth
* \param nid node id
* \param pass_rchild whether right child is not counted in depth
*/
inline int GetDepth(int nid, bool pass_rchild = false) const {
int depth = 0;
while (!nodes[nid].is_root()) {
if (!pass_rchild || nodes[nid].is_left_child()) ++depth;
nid = nodes[nid].parent();
}
return depth;
}
/*!
* \brief get maximum depth
* \param nid node id
*/
inline int MaxDepth(int nid) const {
if (nodes[nid].is_leaf()) return 0;
return std::max(MaxDepth(nodes[nid].cleft())+1,
MaxDepth(nodes[nid].cright())+1);
}
/*!
* \brief get maximum depth
*/
inline int MaxDepth() {
int maxd = 0;
for (int i = 0; i < param.num_roots; ++i) {
maxd = std::max(maxd, MaxDepth(i));
}
return maxd;
}
/*! \brief number of extra nodes besides the root */
inline int num_extra_nodes() const {
return param.num_nodes - param.num_roots - param.num_deleted;
}
};
/*! \brief node statistics used in regression tree */
struct RTreeNodeStat {
/*! \brief loss change caused by current split */
bst_float loss_chg;
/*! \brief sum of hessian values, used to measure coverage of data */
bst_float sum_hess;
/*! \brief weight of current node */
bst_float base_weight;
/*! \brief number of child that is leaf node known up to now */
int leaf_child_cnt;
};
// Used by TreeShap
// data we keep about our decision path
// note that pweight is included for convenience and is not tied with the other attributes
// the pweight of the i'th path element is the permuation weight of paths with i-1 ones in them
struct PathElement {
int feature_index;
bst_float zero_fraction;
bst_float one_fraction;
bst_float pweight;
PathElement() {}
PathElement(int i, bst_float z, bst_float o, bst_float w) :
feature_index(i), zero_fraction(z), one_fraction(o), pweight(w) {}
};
/*!
* \brief define regression tree to be the most common tree model.
* This is the data structure used in xgboost's major tree models.
*/
class RegTree: public TreeModel<bst_float, RTreeNodeStat> {
public:
/*!
* \brief dense feature vector that can be taken by RegTree
* and can be construct from sparse feature vector.
*/
struct FVec {
public:
/*!
* \brief initialize the vector with size vector
* \param size The size of the feature vector.
*/
inline void Init(size_t size);
/*!
* \brief fill the vector with sparse vector
* \param inst The sparse instance to fill.
*/
inline void Fill(const RowBatch::Inst& inst);
/*!
* \brief drop the trace after fill, must be called after fill.
* \param inst The sparse instance to drop.
*/
inline void Drop(const RowBatch::Inst& inst);
/*!
* \brief returns the size of the feature vector
* \return the size of the feature vector
*/
inline size_t size() const;
/*!
* \brief get ith value
* \param i feature index.
* \return the i-th feature value
*/
inline bst_float fvalue(size_t i) const;
/*!
* \brief check whether i-th entry is missing
* \param i feature index.
* \return whether i-th value is missing.
*/
inline bool is_missing(size_t i) const;
private:
/*!
* \brief a union value of value and flag
* when flag == -1, this indicate the value is missing
*/
union Entry {
bst_float fvalue;
int flag;
};
std::vector<Entry> data;
};
/*!
* \brief get the leaf index
* \param feat dense feature vector, if the feature is missing the field is set to NaN
* \param root_id starting root index of the instance
* \return the leaf index of the given feature
*/
inline int GetLeafIndex(const FVec& feat, unsigned root_id = 0) const;
/*!
* \brief get the prediction of regression tree, only accepts dense feature vector
* \param feat dense feature vector, if the feature is missing the field is set to NaN
* \param root_id starting root index of the instance
* \return the leaf index of the given feature
*/
inline bst_float Predict(const FVec& feat, unsigned root_id = 0) const;
/*!
* \brief calculate the feature contributions (https://arxiv.org/abs/1706.06060) for the tree
* \param feat dense feature vector, if the feature is missing the field is set to NaN
* \param root_id starting root index of the instance
* \param out_contribs output vector to hold the contributions
*/
inline void CalculateContributions(const RegTree::FVec& feat, unsigned root_id,
bst_float *out_contribs) const;
inline void TreeShap(const RegTree::FVec& feat, bst_float *phi,
unsigned node_index, unsigned unique_depth,
PathElement *parent_unique_path, bst_float parent_zero_fraction,
bst_float parent_one_fraction, int parent_feature_index) const;
/*!
* \brief calculate the approximate feature contributions for the given root
* \param feat dense feature vector, if the feature is missing the field is set to NaN
* \param root_id starting root index of the instance
* \param out_contribs output vector to hold the contributions
*/
inline void CalculateContributionsApprox(const RegTree::FVec& feat, unsigned root_id,
bst_float *out_contribs) const;
/*!
* \brief get next position of the tree given current pid
* \param pid Current node id.
* \param fvalue feature value if not missing.
* \param is_unknown Whether current required feature is missing.
*/
inline int GetNext(int pid, bst_float fvalue, bool is_unknown) const;
/*!
* \brief dump the model in the requested format as a text string
* \param fmap feature map that may help give interpretations of feature
* \param with_stats whether dump out statistics as well
* \param format the format to dump the model in
* \return the string of dumped model
*/
std::string DumpModel(const FeatureMap& fmap,
bool with_stats,
std::string format) const;
/*!
* \brief calculate the mean value for each node, required for feature contributions
*/
inline void FillNodeMeanValues();
private:
inline bst_float FillNodeMeanValue(int nid);
std::vector<bst_float> node_mean_values;
};
// implementations of inline functions
// do not need to read if only use the model
inline void RegTree::FVec::Init(size_t size) {
Entry e; e.flag = -1;
data.resize(size);
std::fill(data.begin(), data.end(), e);
}
inline void RegTree::FVec::Fill(const RowBatch::Inst& inst) {
for (bst_uint i = 0; i < inst.length; ++i) {
if (inst[i].index >= data.size()) continue;
data[inst[i].index].fvalue = inst[i].fvalue;
}
}
inline void RegTree::FVec::Drop(const RowBatch::Inst& inst) {
for (bst_uint i = 0; i < inst.length; ++i) {
if (inst[i].index >= data.size()) continue;
data[inst[i].index].flag = -1;
}
}
inline size_t RegTree::FVec::size() const {
return data.size();
}
inline bst_float RegTree::FVec::fvalue(size_t i) const {
return data[i].fvalue;
}
inline bool RegTree::FVec::is_missing(size_t i) const {
return data[i].flag == -1;
}
inline int RegTree::GetLeafIndex(const RegTree::FVec& feat, unsigned root_id) const {
int pid = static_cast<int>(root_id);
while (!(*this)[pid].is_leaf()) {
unsigned split_index = (*this)[pid].split_index();
pid = this->GetNext(pid, feat.fvalue(split_index), feat.is_missing(split_index));
}
return pid;
}
inline bst_float RegTree::Predict(const RegTree::FVec& feat, unsigned root_id) const {
int pid = this->GetLeafIndex(feat, root_id);
return (*this)[pid].leaf_value();
}
inline void RegTree::FillNodeMeanValues() {
size_t num_nodes = this->param.num_nodes;
if (this->node_mean_values.size() == num_nodes) {
return;
}
this->node_mean_values.resize(num_nodes);
for (int root_id = 0; root_id < param.num_roots; ++root_id) {
this->FillNodeMeanValue(root_id);
}
}
inline bst_float RegTree::FillNodeMeanValue(int nid) {
bst_float result;
auto& node = (*this)[nid];
if (node.is_leaf()) {
result = node.leaf_value();
} else {
result = this->FillNodeMeanValue(node.cleft()) * this->stat(node.cleft()).sum_hess;
result += this->FillNodeMeanValue(node.cright()) * this->stat(node.cright()).sum_hess;
result /= this->stat(nid).sum_hess;
}
this->node_mean_values[nid] = result;
return result;
}
inline void RegTree::CalculateContributionsApprox(const RegTree::FVec& feat, unsigned root_id,
bst_float *out_contribs) const {
CHECK_GT(this->node_mean_values.size(), 0U);
// this follows the idea of http://blog.datadive.net/interpreting-random-forests/
bst_float node_value;
unsigned split_index;
int pid = static_cast<int>(root_id);
// update bias value
node_value = this->node_mean_values[pid];
out_contribs[feat.size()] += node_value;
if ((*this)[pid].is_leaf()) {
// nothing to do anymore
return;
}
while (!(*this)[pid].is_leaf()) {
split_index = (*this)[pid].split_index();
pid = this->GetNext(pid, feat.fvalue(split_index), feat.is_missing(split_index));
bst_float new_value = this->node_mean_values[pid];
// update feature weight
out_contribs[split_index] += new_value - node_value;
node_value = new_value;
}
bst_float leaf_value = (*this)[pid].leaf_value();
// update leaf feature weight
out_contribs[split_index] += leaf_value - node_value;
}
// extend our decision path with a fraction of one and zero extensions
inline void ExtendPath(PathElement *unique_path, unsigned unique_depth,
bst_float zero_fraction, bst_float one_fraction, int feature_index) {
unique_path[unique_depth].feature_index = feature_index;
unique_path[unique_depth].zero_fraction = zero_fraction;
unique_path[unique_depth].one_fraction = one_fraction;
unique_path[unique_depth].pweight = static_cast<bst_float>(unique_depth == 0 ? 1 : 0);
for (int i = unique_depth-1; i >= 0; i--) {
unique_path[i+1].pweight += one_fraction*unique_path[i].pweight*(i+1)
/ static_cast<bst_float>(unique_depth+1);
unique_path[i].pweight = zero_fraction*unique_path[i].pweight*(unique_depth-i)
/ static_cast<bst_float>(unique_depth+1);
}
}
// undo a previous extension of the decision path
inline void UnwindPath(PathElement *unique_path, unsigned unique_depth, unsigned path_index) {
const bst_float one_fraction = unique_path[path_index].one_fraction;
const bst_float zero_fraction = unique_path[path_index].zero_fraction;
bst_float next_one_portion = unique_path[unique_depth].pweight;
for (int i = unique_depth-1; i >= 0; --i) {
if (one_fraction != 0) {
const bst_float tmp = unique_path[i].pweight;
unique_path[i].pweight = next_one_portion*(unique_depth+1)
/ static_cast<bst_float>((i+1)*one_fraction);
next_one_portion = tmp - unique_path[i].pweight*zero_fraction*(unique_depth-i)
/ static_cast<bst_float>(unique_depth+1);
} else {
unique_path[i].pweight = (unique_path[i].pweight*(unique_depth+1))
/ static_cast<bst_float>(zero_fraction*(unique_depth-i));
}
}
for (auto i = path_index; i < unique_depth; ++i) {
unique_path[i].feature_index = unique_path[i+1].feature_index;
unique_path[i].zero_fraction = unique_path[i+1].zero_fraction;
unique_path[i].one_fraction = unique_path[i+1].one_fraction;
}
}
// determine what the total permuation weight would be if
// we unwound a previous extension in the decision path
inline bst_float UnwoundPathSum(const PathElement *unique_path, unsigned unique_depth,
unsigned path_index) {
const bst_float one_fraction = unique_path[path_index].one_fraction;
const bst_float zero_fraction = unique_path[path_index].zero_fraction;
bst_float next_one_portion = unique_path[unique_depth].pweight;
bst_float total = 0;
for (int i = unique_depth-1; i >= 0; --i) {
if (one_fraction != 0) {
const bst_float tmp = next_one_portion*(unique_depth+1)
/ static_cast<bst_float>((i+1)*one_fraction);
total += tmp;
next_one_portion = unique_path[i].pweight - tmp*zero_fraction*((unique_depth-i)
/ static_cast<bst_float>(unique_depth+1));
} else {
total += (unique_path[i].pweight/zero_fraction)/((unique_depth-i)
/ static_cast<bst_float>(unique_depth+1));
}
}
return total;
}
// recursive computation of SHAP values for a decision tree
inline void RegTree::TreeShap(const RegTree::FVec& feat, bst_float *phi,
unsigned node_index, unsigned unique_depth,
PathElement *parent_unique_path, bst_float parent_zero_fraction,
bst_float parent_one_fraction, int parent_feature_index) const {
const auto node = (*this)[node_index];
// extend the unique path
PathElement *unique_path = parent_unique_path + unique_depth;
if (unique_depth > 0) std::copy(parent_unique_path, parent_unique_path+unique_depth, unique_path);
ExtendPath(unique_path, unique_depth, parent_zero_fraction,
parent_one_fraction, parent_feature_index);
const unsigned split_index = node.split_index();
// leaf node
if (node.is_leaf()) {
for (unsigned i = 1; i <= unique_depth; ++i) {
const bst_float w = UnwoundPathSum(unique_path, unique_depth, i);
const PathElement &el = unique_path[i];
phi[el.feature_index] += w*(el.one_fraction-el.zero_fraction)*node.leaf_value();
}
// internal node
} else {
// find which branch is "hot" (meaning x would follow it)
unsigned hot_index = 0;
if (feat.is_missing(split_index)) {
hot_index = node.cdefault();
} else if (feat.fvalue(split_index) < node.split_cond()) {
hot_index = node.cleft();
} else {
hot_index = node.cright();
}
const unsigned cold_index = (hot_index == node.cleft() ? node.cright() : node.cleft());
const bst_float w = this->stat(node_index).sum_hess;
const bst_float hot_zero_fraction = this->stat(hot_index).sum_hess/w;
const bst_float cold_zero_fraction = this->stat(cold_index).sum_hess/w;
bst_float incoming_zero_fraction = 1;
bst_float incoming_one_fraction = 1;
// see if we have already split on this feature,
// if so we undo that split so we can redo it for this node
unsigned path_index = 0;
for (; path_index <= unique_depth; ++path_index) {
if (unique_path[path_index].feature_index == split_index) break;
}
if (path_index != unique_depth+1) {
incoming_zero_fraction = unique_path[path_index].zero_fraction;
incoming_one_fraction = unique_path[path_index].one_fraction;
UnwindPath(unique_path, unique_depth, path_index);
unique_depth -= 1;
}
TreeShap(feat, phi, hot_index, unique_depth+1, unique_path,
hot_zero_fraction*incoming_zero_fraction, incoming_one_fraction, split_index);
TreeShap(feat, phi, cold_index, unique_depth+1, unique_path,
cold_zero_fraction*incoming_zero_fraction, 0, split_index);
}
}
inline void RegTree::CalculateContributions(const RegTree::FVec& feat, unsigned root_id,
bst_float *out_contribs) const {
// find the expected value of the tree's predictions
bst_float base_value = 0.0;
bst_float total_cover = 0;
for (int i = 0; i < (*this).param.num_nodes; ++i) {
const auto node = (*this)[i];
if (node.is_leaf()) {
const auto cover = this->stat(i).sum_hess;
base_value += cover*node.leaf_value();
total_cover += cover;
}
}
out_contribs[feat.size()] += base_value / total_cover;
// Preallocate space for the unique path data
const int maxd = this->MaxDepth(root_id)+1;
PathElement *unique_path_data = new PathElement[(maxd*(maxd+1))/2];
TreeShap(feat, out_contribs, root_id, 0, unique_path_data, 1, 1, -1);
delete[] unique_path_data;
}
/*! \brief get next position of the tree given current pid */
inline int RegTree::GetNext(int pid, bst_float fvalue, bool is_unknown) const {
bst_float split_value = (*this)[pid].split_cond();
if (is_unknown) {
return (*this)[pid].cdefault();
} else {
if (fvalue < split_value) {
return (*this)[pid].cleft();
} else {
return (*this)[pid].cright();
}
}
}
} // namespace xgboost
#endif // XGBOOST_TREE_MODEL_H_
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