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

Add data split mode to DMatrix MetaInfo (#8568)

Browse Source
This commit is contained in:
Rong Ou
2022-12-25 04:37:37 -08:00
committed by GitHub
parent 77b069c25d
commit 3ceeb8c61c
20 changed files with 113 additions and 103 deletions
Download Patch File Download Diff File Expand all files Collapse all files

53
src/learner.cc
View File

@@ -273,8 +273,6 @@ void LearnerModelParam::Copy(LearnerModelParam const& that) {
}
struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
// data split mode, can be row, col, or none.
DataSplitMode dsplit {DataSplitMode::kAuto};
// flag to disable default metric
bool disable_default_eval_metric {false};
// FIXME(trivialfis): The following parameters belong to model itself, but can be
@@ -284,13 +282,6 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
// declare parameters
DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
DMLC_DECLARE_FIELD(dsplit)
.set_default(DataSplitMode::kAuto)
.add_enum("auto", DataSplitMode::kAuto)
.add_enum("col", DataSplitMode::kCol)
.add_enum("row", DataSplitMode::kRow)
.add_enum("none", DataSplitMode::kNone)
.describe("Data split mode for distributed training.");
DMLC_DECLARE_FIELD(disable_default_eval_metric)
.set_default(false)
.describe("Flag to disable default metric. Set to >0 to disable");
@@ -445,12 +436,6 @@ class LearnerConfiguration : public Learner {
ConsoleLogger::Configure(args);
// add additional parameters
// These are cosntraints that need to be satisfied.
if (tparam_.dsplit == DataSplitMode::kAuto && collective::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
}
// set seed only before the model is initialized
if (!initialized || ctx_.seed != old_seed) {
common::GlobalRandom().seed(ctx_.seed);
@@ -1055,11 +1040,6 @@ class LearnerIO : public LearnerConfiguration {
auto n = tparam_.__DICT__();
cfg_.insert(n.cbegin(), n.cend());
// copy dsplit from config since it will not run again during restore
if (tparam_.dsplit == DataSplitMode::kAuto && collective::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
}
this->need_configuration_ = true;
}
@@ -1199,16 +1179,6 @@ class LearnerImpl : public LearnerIO {
local_map->erase(this);
}
}
// Configuration before data is known.
void CheckDataSplitMode() {
if (collective::IsDistributed()) {
CHECK(tparam_.dsplit != DataSplitMode::kAuto)
<< "Precondition violated; dsplit cannot be 'auto' in distributed mode";
if (tparam_.dsplit == DataSplitMode::kCol) {
LOG(FATAL) << "Column-wise data split is currently not supported.";
}
}
}
std::vector<std::string> DumpModel(const FeatureMap& fmap, bool with_stats,
std::string format) override {
@@ -1266,7 +1236,6 @@ class LearnerImpl : public LearnerIO {
common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);
}
this->CheckDataSplitMode();
this->ValidateDMatrix(train.get(), true);
auto local_cache = this->GetPredictionCache();
@@ -1295,7 +1264,6 @@ class LearnerImpl : public LearnerIO {
common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);
}
this->CheckDataSplitMode();
this->ValidateDMatrix(train.get(), true);
auto local_cache = this->GetPredictionCache();
@@ -1444,19 +1412,14 @@ class LearnerImpl : public LearnerIO {
MetaInfo const& info = p_fmat->Info();
info.Validate(ctx_.gpu_id);
auto const row_based_split = [this]() {
return tparam_.dsplit == DataSplitMode::kRow || tparam_.dsplit == DataSplitMode::kAuto;
};
if (row_based_split()) {
if (is_training) {
CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_)
<< "Number of columns does not match number of features in "
"booster.";
} else {
CHECK_GE(learner_model_param_.num_feature, p_fmat->Info().num_col_)
<< "Number of columns does not match number of features in "
"booster.";
}
if (is_training) {
CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_)
<< "Number of columns does not match number of features in "
"booster.";
} else {
CHECK_GE(learner_model_param_.num_feature, p_fmat->Info().num_col_)
<< "Number of columns does not match number of features in "
"booster.";
}
if (p_fmat->Info().num_row_ == 0) {