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[breaking] Add prediction fucntion for DMatrix and use inplace predict for dask. (#6668)

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* Add a new API function for predicting on `DMatrix`.  This function aligns
with rest of the `XGBoosterPredictFrom*` functions on semantic of function
arguments.
* Purge `ntree_limit` from libxgboost, use iteration instead.
* [dask] Use `inplace_predict` by default for dask sklearn models.
* [dask] Run prediction shape inference on worker instead of client.

The breaking change is in the Python sklearn `apply` function, I made it to be
consistent with other prediction functions where `best_iteration` is used by
default.
This commit is contained in:
Jiaming Yuan
2021-02-08 18:26:32 +08:00
committed by GitHub
parent dbb5208a0a
commit 4656b09d5d
29 changed files with 1134 additions and 604 deletions
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54
src/predictor/cpu_predictor.cc
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@@ -234,56 +234,28 @@ class CPUPredictor : public Predictor {
public:
explicit CPUPredictor(GenericParameter const* generic_param) :
Predictor::Predictor{generic_param} {}
// ntree_limit is a very problematic parameter, as it's ambiguous in the context of
// multi-output and forest. Same problem exists for tree_begin
void PredictBatch(DMatrix* dmat, PredictionCacheEntry* predts,
const gbm::GBTreeModel& model, int tree_begin,
uint32_t const ntree_limit = 0) const override {
// tree_begin is not used, right now we just enforce it to be 0.
CHECK_EQ(tree_begin, 0);
void PredictBatch(DMatrix *dmat, PredictionCacheEntry *predts,
const gbm::GBTreeModel &model, uint32_t tree_begin,
uint32_t tree_end = 0) const override {
auto* out_preds = &predts->predictions;
CHECK_GE(predts->version, tree_begin);
if (out_preds->Size() == 0 && dmat->Info().num_row_ != 0) {
CHECK_EQ(predts->version, 0);
}
// This is actually already handled in gbm, but large amount of tests rely on the
// behaviour.
if (tree_end == 0) {
tree_end = model.trees.size();
}
if (predts->version == 0) {
// out_preds->Size() can be non-zero as it's initialized here before any tree is
// built at the 0^th iterator.
this->InitOutPredictions(dmat->Info(), out_preds, model);
}
uint32_t const output_groups = model.learner_model_param->num_output_group;
CHECK_NE(output_groups, 0);
// Right now we just assume ntree_limit provided by users means number of tree layers
// in the context of multi-output model
uint32_t real_ntree_limit = ntree_limit * output_groups;
if (real_ntree_limit == 0 || real_ntree_limit > model.trees.size()) {
real_ntree_limit = static_cast<uint32_t>(model.trees.size());
if (tree_end - tree_begin == 0) {
return;
}
uint32_t const end_version = (tree_begin + real_ntree_limit) / output_groups;
// When users have provided ntree_limit, end_version can be lesser, cache is violated
if (predts->version > end_version) {
CHECK_NE(ntree_limit, 0);
this->InitOutPredictions(dmat->Info(), out_preds, model);
predts->version = 0;
}
uint32_t const beg_version = predts->version;
CHECK_LE(beg_version, end_version);
if (beg_version < end_version) {
this->PredictDMatrix(dmat, &out_preds->HostVector(), model,
beg_version * output_groups,
end_version * output_groups);
}
// delta means {size of forest} * {number of newly accumulated layers}
uint32_t delta = end_version - beg_version;
CHECK_LE(delta, model.trees.size());
predts->Update(delta);
CHECK(out_preds->Size() == output_groups * dmat->Info().num_row_ ||
out_preds->Size() == dmat->Info().num_row_);
this->PredictDMatrix(dmat, &out_preds->HostVector(), model, tree_begin,
tree_end);
}
template <typename Adapter>
@@ -362,7 +334,6 @@ class CPUPredictor : public Predictor {
InitThreadTemp(nthread, model.learner_model_param->num_feature, &feat_vecs);
const MetaInfo& info = p_fmat->Info();
// number of valid trees
ntree_limit *= model.learner_model_param->num_output_group;
if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
ntree_limit = static_cast<unsigned>(model.trees.size());
}
@@ -398,7 +369,6 @@ class CPUPredictor : public Predictor {
InitThreadTemp(nthread, model.learner_model_param->num_feature, &feat_vecs);
const MetaInfo& info = p_fmat->Info();
// number of valid trees
ntree_limit *= model.learner_model_param->num_output_group;
if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
ntree_limit = static_cast<unsigned>(model.trees.size());
}