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Initial support for multioutput regression. (#7514)

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* Add num target model parameter, which is configured from input labels.
* Change elementwise metric and indexing for weights.
* Add demo.
* Add tests.
This commit is contained in:
Jiaming Yuan
2021-12-18 09:28:38 +08:00
committed by GitHub
parent 9ab73f737e
commit 58a6723eb1
22 changed files with 306 additions and 67 deletions
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69
src/learner.cc
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@@ -88,12 +88,15 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
/*! \brief the version of XGBoost. */
uint32_t major_version;
uint32_t minor_version;
uint32_t num_target{1};
/*! \brief reserved field */
int reserved[27];
int reserved[26];
/*! \brief constructor */
LearnerModelParamLegacy() {
std::memset(this, 0, sizeof(LearnerModelParamLegacy));
base_score = 0.5f;
num_target = 1;
major_version = std::get<0>(Version::Self());
minor_version = std::get<1>(Version::Self());
static_assert(sizeof(LearnerModelParamLegacy) == 136,
@@ -119,6 +122,12 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
CHECK(ret.ec == std::errc());
obj["num_class"] =
std::string{integers, static_cast<size_t>(std::distance(integers, ret.ptr))};
ret = to_chars(integers, integers + NumericLimits<int64_t>::kToCharsSize,
static_cast<int64_t>(num_target));
obj["num_target"] =
std::string{integers, static_cast<size_t>(std::distance(integers, ret.ptr))};
return Json(std::move(obj));
}
void FromJson(Json const& obj) {
@@ -126,6 +135,11 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
std::map<std::string, std::string> m;
m["num_feature"] = get<String const>(j_param.at("num_feature"));
m["num_class"] = get<String const>(j_param.at("num_class"));
auto n_targets_it = j_param.find("num_target");
if (n_targets_it != j_param.cend()) {
m["num_target"] = get<String const>(n_targets_it->second);
}
this->Init(m);
std::string str = get<String const>(j_param.at("base_score"));
from_chars(str.c_str(), str.c_str() + str.size(), base_score);
@@ -139,6 +153,7 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
dmlc::ByteSwap(&x.contain_eval_metrics, sizeof(x.contain_eval_metrics), 1);
dmlc::ByteSwap(&x.major_version, sizeof(x.major_version), 1);
dmlc::ByteSwap(&x.minor_version, sizeof(x.minor_version), 1);
dmlc::ByteSwap(&x.num_target, sizeof(x.num_target), 1);
dmlc::ByteSwap(x.reserved, sizeof(x.reserved[0]), sizeof(x.reserved) / sizeof(x.reserved[0]));
return x;
}
@@ -156,15 +171,24 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
DMLC_DECLARE_FIELD(num_class).set_default(0).set_lower_bound(0).describe(
"Number of class option for multi-class classifier. "
" By default equals 0 and corresponds to binary classifier.");
DMLC_DECLARE_FIELD(num_target)
.set_default(1)
.set_lower_bound(1)
.describe("Number of target for multi-target regression.");
}
};
LearnerModelParam::LearnerModelParam(LearnerModelParamLegacy const& user_param, float base_margin,
ObjInfo t)
: base_score{base_margin},
num_feature{user_param.num_feature},
num_output_group{user_param.num_class == 0 ? 1 : static_cast<uint32_t>(user_param.num_class)},
task{t} {}
: base_score{base_margin}, num_feature{user_param.num_feature}, task{t} {
auto n_classes = std::max(static_cast<uint32_t>(user_param.num_class), 1u);
auto n_targets = user_param.num_target;
num_output_group = std::max(n_classes, n_targets);
// For version < 1.6, n_targets == 0
CHECK(n_classes <= 1 || n_targets <= 1)
<< "Multi-class multi-output is not yet supported. n_classes:" << n_classes
<< ", n_targets:" << n_targets;
}
struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
// data split mode, can be row, col, or none.
@@ -325,6 +349,8 @@ class LearnerConfiguration : public Learner {
args = {cfg_.cbegin(), cfg_.cend()}; // renew
this->ConfigureObjective(old_tparam, &args);
auto task = this->ConfigureTargets();
// Before 1.0.0, we save `base_score` into binary as a transformed value by objective.
// After 1.0.0 we save the value provided by user and keep it immutable instead. To
// keep the stability, we initialize it in binary LoadModel instead of configuration.
@@ -339,7 +365,7 @@ class LearnerConfiguration : public Learner {
// - model is configured second time due to change of parameter
if (!learner_model_param_.Initialized() || mparam_.base_score != mparam_backup.base_score) {
learner_model_param_ =
LearnerModelParam(mparam_, obj_->ProbToMargin(mparam_.base_score), obj_->Task());
LearnerModelParam(mparam_, obj_->ProbToMargin(mparam_.base_score), task);
}
this->ConfigureGBM(old_tparam, args);
@@ -586,8 +612,7 @@ class LearnerConfiguration : public Learner {
CHECK(matrix.first);
CHECK(!matrix.second.ref.expired());
const uint64_t num_col = matrix.first->Info().num_col_;
CHECK_LE(num_col,
static_cast<uint64_t>(std::numeric_limits<unsigned>::max()))
CHECK_LE(num_col, static_cast<uint64_t>(std::numeric_limits<unsigned>::max()))
<< "Unfortunately, XGBoost does not support data matrices with "
<< std::numeric_limits<unsigned>::max() << " features or greater";
num_feature = std::max(num_feature, static_cast<uint32_t>(num_col));
@@ -652,6 +677,31 @@ class LearnerConfiguration : public Learner {
p_metric->Configure(args);
}
}
/**
* Get number of targets from objective function.
*/
ObjInfo ConfigureTargets() {
CHECK(this->obj_);
auto const& cache = this->GetPredictionCache()->Container();
size_t n_targets = 1;
for (auto const& d : cache) {
if (n_targets == 1) {
n_targets = this->obj_->Targets(d.first->Info());
} else {
auto t = this->obj_->Targets(d.first->Info());
CHECK(n_targets == t || 1 == t) << "Inconsistent labels.";
}
}
if (mparam_.num_target != 1) {
CHECK(n_targets == 1 || n_targets == mparam_.num_target)
<< "Inconsistent configuration of num_target. Configuration result from input data:"
<< n_targets << ", configuration from parameter:" << mparam_.num_target;
} else {
mparam_.num_target = n_targets;
}
return this->obj_->Task();
}
};
std::string const LearnerConfiguration::kEvalMetric {"eval_metric"}; // NOLINT
@@ -784,6 +834,9 @@ class LearnerIO : public LearnerConfiguration {
if (!DMLC_IO_NO_ENDIAN_SWAP) {
mparam_ = mparam_.ByteSwap();
}
if (mparam_.num_target == 0) {
mparam_.num_target = 1;
}
CHECK(fi->Read(&tparam_.objective)) << "BoostLearner: wrong model format";
CHECK(fi->Read(&tparam_.booster)) << "BoostLearner: wrong model format";