Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update GPUTreeshap (#6163)

Browse Source 
* Reduce shap test duration

* Test interoperability with shap package

* Add feature interactions

* Update GPUTreeShap
This commit is contained in:
Rory Mitchell
2020-09-28 09:43:47 +13:00
committed by GitHub
parent 434a3f35a3
commit dda9e1e487
14 changed files with 176 additions and 87 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
src/predictor/cpu_predictor.cc
View File

@@ -352,7 +352,7 @@ class CPUPredictor : public Predictor {
}
}
void PredictContribution(DMatrix* p_fmat, std::vector<bst_float>* out_contribs,
void PredictContribution(DMatrix* p_fmat, HostDeviceVector<float>* out_contribs,
const gbm::GBTreeModel& model, uint32_t ntree_limit,
std::vector<bst_float>* tree_weights,
bool approximate, int condition,
@@ -370,7 +370,7 @@ class CPUPredictor : public Predictor {
size_t const ncolumns = model.learner_model_param->num_feature + 1;
CHECK_NE(ncolumns, 0);
// allocate space for (number of features + bias) times the number of rows
std::vector<bst_float>& contribs = *out_contribs;
std::vector<bst_float>& contribs = out_contribs->HostVector();
contribs.resize(info.num_row_ * ncolumns * model.learner_model_param->num_output_group);
// make sure contributions is zeroed, we could be reusing a previously
// allocated one
@@ -423,7 +423,7 @@ class CPUPredictor : public Predictor {
}
}
void PredictInteractionContributions(DMatrix* p_fmat, std::vector<bst_float>* out_contribs,
void PredictInteractionContributions(DMatrix* p_fmat, HostDeviceVector<bst_float>* out_contribs,
const gbm::GBTreeModel& model, unsigned ntree_limit,
std::vector<bst_float>* tree_weights,
bool approximate) override {
@@ -435,21 +435,24 @@ class CPUPredictor : public Predictor {
const unsigned crow_chunk = ngroup * (ncolumns + 1);
// allocate space for (number of features^2) times the number of rows and tmp off/on contribs
std::vector<bst_float>& contribs = *out_contribs;
std::vector<bst_float>& contribs = out_contribs->HostVector();
contribs.resize(info.num_row_ * ngroup * (ncolumns + 1) * (ncolumns + 1));
std::vector<bst_float> contribs_off(info.num_row_ * ngroup * (ncolumns + 1));
std::vector<bst_float> contribs_on(info.num_row_ * ngroup * (ncolumns + 1));
std::vector<bst_float> contribs_diag(info.num_row_ * ngroup * (ncolumns + 1));
HostDeviceVector<bst_float> contribs_off_hdv(info.num_row_ * ngroup * (ncolumns + 1));
auto &contribs_off = contribs_off_hdv.HostVector();
HostDeviceVector<bst_float> contribs_on_hdv(info.num_row_ * ngroup * (ncolumns + 1));
auto &contribs_on = contribs_on_hdv.HostVector();
HostDeviceVector<bst_float> contribs_diag_hdv(info.num_row_ * ngroup * (ncolumns + 1));
auto &contribs_diag = contribs_diag_hdv.HostVector();
// Compute the difference in effects when conditioning on each of the features on and off
// see: Axiomatic characterizations of probabilistic and
// cardinal-probabilistic interaction indices
PredictContribution(p_fmat, &contribs_diag, model, ntree_limit,
PredictContribution(p_fmat, &contribs_diag_hdv, model, ntree_limit,
tree_weights, approximate, 0, 0);
for (size_t i = 0; i < ncolumns + 1; ++i) {
PredictContribution(p_fmat, &contribs_off, model, ntree_limit,
PredictContribution(p_fmat, &contribs_off_hdv, model, ntree_limit,
tree_weights, approximate, -1, i);
PredictContribution(p_fmat, &contribs_on, model, ntree_limit,
PredictContribution(p_fmat, &contribs_on_hdv, model, ntree_limit,
tree_weights, approximate, 1, i);
for (size_t j = 0; j < info.num_row_; ++j) {