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Support multi-class with base margin. (#7381)

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This is already partially supported but never properly tested. So the only possible way to use it is calling `numpy.ndarray.flatten` with `base_margin` before passing it into XGBoost. This PR adds proper support
for most of the data types along with tests.
This commit is contained in:
Jiaming Yuan
2021-11-02 13:38:00 +08:00
committed by GitHub
parent 6295dc3b67
commit a13321148a
18 changed files with 274 additions and 92 deletions
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27
src/predictor/cpu_predictor.cc
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@@ -290,27 +290,16 @@ class CPUPredictor : public Predictor {
const auto& base_margin = info.base_margin_.HostVector();
out_preds->Resize(n);
std::vector<bst_float>& out_preds_h = out_preds->HostVector();
if (base_margin.size() == n) {
CHECK_EQ(out_preds->Size(), n);
std::copy(base_margin.begin(), base_margin.end(), out_preds_h.begin());
} else {
if (!base_margin.empty()) {
std::ostringstream oss;
oss << "Ignoring the base margin, since it has incorrect length. "
<< "The base margin must be an array of length ";
if (model.learner_model_param->num_output_group > 1) {
oss << "[num_class] * [number of data points], i.e. "
<< model.learner_model_param->num_output_group << " * " << info.num_row_
<< " = " << n << ". ";
} else {
oss << "[number of data points], i.e. " << info.num_row_ << ". ";
}
oss << "Instead, all data points will use "
<< "base_score = " << model.learner_model_param->base_score;
LOG(WARNING) << oss.str();
}
if (base_margin.empty()) {
std::fill(out_preds_h.begin(), out_preds_h.end(),
model.learner_model_param->base_score);
} else {
std::string expected{
"(" + std::to_string(info.num_row_) + ", " +
std::to_string(model.learner_model_param->num_output_group) + ")"};
CHECK_EQ(base_margin.size(), n)
<< "Invalid shape of base_margin. Expected:" << expected;
std::copy(base_margin.begin(), base_margin.end(), out_preds_h.begin());
}
}