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GroupSHAP option #11595

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2 changes: 1 addition & 1 deletion include/xgboost/predictor.h
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Original file line number Diff line number Diff line change
Expand Up @@ -158,7 +158,7 @@ class Predictor {
gbm::GBTreeModel const& model, bst_tree_t tree_end = 0,
std::vector<float> const* tree_weights = nullptr,
bool approximate = false, int condition = 0,
unsigned condition_feature = 0) const = 0;
unsigned condition_feature = 0, HostDeviceVector<bst_feature_t>* feature_reprs = nullptr) const = 0;

virtual void PredictInteractionContributions(DMatrix* dmat, HostDeviceVector<float>* out_contribs,
gbm::GBTreeModel const& model,
Expand Down
10 changes: 5 additions & 5 deletions src/predictor/cpu_predictor.cc
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Expand Up @@ -747,7 +747,7 @@ class CPUPredictor : public Predictor {
DataView batch, const MetaInfo &info, const gbm::GBTreeModel &model,
const std::vector<bst_float> *tree_weights, std::vector<std::vector<float>> *mean_values,
std::vector<RegTree::FVec> *feat_vecs, std::vector<bst_float> *contribs,
bst_tree_t ntree_limit, bool approximate, int condition, unsigned condition_feature) const {
bst_tree_t ntree_limit, bool approximate, int condition, unsigned condition_feature, std::vector<bst_feature_t> *feature_reprs = nullptr) const {
const int num_feature = model.learner_model_param->num_feature;
const int ngroup = model.learner_model_param->num_output_group;
CHECK_NE(ngroup, 0);
Expand Down Expand Up @@ -778,7 +778,7 @@ class CPUPredictor : public Predictor {
}
if (!approximate) {
CalculateContributions(*model.trees[j], feats, tree_mean_values,
&this_tree_contribs[0], condition, condition_feature);
&this_tree_contribs[0], condition, condition_feature, feature_reprs == nullptr ? nullptr : feature_reprs->data());
} else {
model.trees[j]->CalculateContributionsApprox(
feats, tree_mean_values, &this_tree_contribs[0]);
Expand Down Expand Up @@ -950,7 +950,7 @@ class CPUPredictor : public Predictor {
void PredictContribution(DMatrix *p_fmat, HostDeviceVector<float> *out_contribs,
const gbm::GBTreeModel &model, bst_tree_t ntree_limit,
std::vector<bst_float> const *tree_weights, bool approximate,
int condition, unsigned condition_feature) const override {
int condition, unsigned condition_feature, HostDeviceVector<bst_feature_t> *feature_reprs = nullptr) const override {
CHECK(!model.learner_model_param->IsVectorLeaf())
<< "Predict contribution" << MTNotImplemented();
CHECK(!p_fmat->Info().IsColumnSplit())
Expand Down Expand Up @@ -982,13 +982,13 @@ class CPUPredictor : public Predictor {
for (const auto &batch : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, {})) {
PredictContributionKernel(GHistIndexMatrixView{batch, std::forward<Enc>(acc), ft}, info,
model, tree_weights, &mean_values, &feat_vecs, &contribs,
ntree_limit, approximate, condition, condition_feature);
ntree_limit, approximate, condition, condition_feature, feature_reprs == nullptr ? nullptr : &feature_reprs->HostVector());
}
} else {
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
PredictContributionKernel(SparsePageView{&batch, std::forward<Enc>(acc)}, info, model,
tree_weights, &mean_values, &feat_vecs, &contribs, ntree_limit,
approximate, condition, condition_feature);
approximate, condition, condition_feature, feature_reprs == nullptr ? nullptr : &feature_reprs->HostVector());
}
}
};
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23 changes: 13 additions & 10 deletions src/predictor/cpu_treeshap.cc
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Expand Up @@ -106,11 +106,13 @@ float UnwoundPathSum(const PathElement* unique_path, std::uint32_t unique_depth,
* \param condition fix one feature to either off (-1) on (1) or not fixed (0 default)
* \param condition_feature the index of the feature to fix
* \param condition_fraction what fraction of the current weight matches our conditioning feature
* \param feature_reprs mapping of features to group representatives, for groupSHAP calculation
*/
void TreeShap(RegTree const& tree, const RegTree::FVec& feat, float* phi, bst_node_t node_index,
std::uint32_t unique_depth, PathElement* parent_unique_path,
float parent_zero_fraction, float parent_one_fraction, int parent_feature_index,
int condition, std::uint32_t condition_feature, float condition_fraction) {
int condition, std::uint32_t condition_feature, float condition_fraction,
std::uint32_t* feature_reprs) {
const auto node = tree[node_index];

// stop if we have no weight coming down to us
Expand All @@ -125,6 +127,7 @@ void TreeShap(RegTree const& tree, const RegTree::FVec& feat, float* phi, bst_no
parent_feature_index);
}
const std::uint32_t split_index = node.SplitIndex();
const std::uint32_t split_index_repr = feature_reprs == nullptr ? split_index : feature_reprs[split_index];

// leaf node
if (node.IsLeaf()) {
Expand Down Expand Up @@ -153,7 +156,7 @@ void TreeShap(RegTree const& tree, const RegTree::FVec& feat, float* phi, bst_no
// if so we undo that split so we can redo it for this node
std::uint32_t path_index = 0;
for (; path_index <= unique_depth; ++path_index) {
if (static_cast<std::uint32_t>(unique_path[path_index].feature_index) == split_index) break;
if (static_cast<std::uint32_t>(unique_path[path_index].feature_index) == split_index_repr) break;
}
if (path_index != unique_depth + 1) {
incoming_zero_fraction = unique_path[path_index].zero_fraction;
Expand All @@ -165,28 +168,28 @@ void TreeShap(RegTree const& tree, const RegTree::FVec& feat, float* phi, bst_no
// divide up the condition_fraction among the recursive calls
float hot_condition_fraction = condition_fraction;
float cold_condition_fraction = condition_fraction;
if (condition > 0 && split_index == condition_feature) {
if (condition > 0 && split_index_repr == condition_feature) {
cold_condition_fraction = 0;
unique_depth -= 1;
} else if (condition < 0 && split_index == condition_feature) {
} else if (condition < 0 && split_index_repr == condition_feature) {
hot_condition_fraction *= hot_zero_fraction;
cold_condition_fraction *= cold_zero_fraction;
unique_depth -= 1;
}

TreeShap(tree, feat, phi, hot_index, unique_depth + 1, unique_path,
hot_zero_fraction * incoming_zero_fraction, incoming_one_fraction, split_index,
condition, condition_feature, hot_condition_fraction);
hot_zero_fraction * incoming_zero_fraction, incoming_one_fraction, split_index_repr,
condition, condition_feature, hot_condition_fraction, feature_reprs);

TreeShap(tree, feat, phi, cold_index, unique_depth + 1, unique_path,
cold_zero_fraction * incoming_zero_fraction, 0, split_index, condition,
condition_feature, cold_condition_fraction);
cold_zero_fraction * incoming_zero_fraction, 0, split_index_repr, condition,
condition_feature, cold_condition_fraction, feature_reprs);
}
}

void CalculateContributions(RegTree const& tree, const RegTree::FVec& feat,
std::vector<float>* mean_values, float* out_contribs, int condition,
std::uint32_t condition_feature) {
std::uint32_t condition_feature, std::uint32_t* feature_reprs) {
// find the expected value of the tree's predictions
if (condition == 0) {
float node_value = (*mean_values)[0];
Expand All @@ -198,6 +201,6 @@ void CalculateContributions(RegTree const& tree, const RegTree::FVec& feat,
std::vector<PathElement> unique_path_data((maxd * (maxd + 1)) / 2);

TreeShap(tree, feat, out_contribs, 0, 0, unique_path_data.data(), 1, 1, -1, condition,
condition_feature, 1);
condition_feature, 1, feature_reprs);
}
} // namespace xgboost
3 changes: 2 additions & 1 deletion src/predictor/cpu_treeshap.h
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Expand Up @@ -14,9 +14,10 @@ namespace xgboost {
* \param out_contribs output vector to hold the contributions
* \param condition fix one feature to either off (-1) on (1) or not fixed (0 default)
* \param condition_feature the index of the feature to fix
* \param feature_reprs mapping of features to group representatives, for groupSHAP calculation
*/
void CalculateContributions(RegTree const &tree, const RegTree::FVec &feat,
std::vector<float> *mean_values, bst_float *out_contribs, int condition,
unsigned condition_feature);
unsigned condition_feature, std::uint32_t* feature_reprs);
} // namespace xgboost
#endif // XGBOOST_PREDICTOR_CPU_TREESHAP_H_