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Calculate base_score based on input labels for mae. (#8107)

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Fit an intercept as base score for abs loss.
This commit is contained in:
Jiaming Yuan
2022-09-20 20:53:54 +08:00
committed by GitHub
parent 4f42aa5f12
commit fffb1fca52
42 changed files with 999 additions and 343 deletions
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36
src/predictor/gpu_predictor.cu
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@@ -511,7 +511,7 @@ void ExtractPaths(
n = d_nodes[n.Parent() + tree_offset];
path_length++;
}
return PathInfo{int64_t(idx), path_length, tree_idx};
return PathInfo{static_cast<int64_t>(idx), path_length, tree_idx};
});
auto end = thrust::copy_if(
thrust::cuda::par(alloc), nodes_transform,
@@ -859,13 +859,13 @@ class GPUPredictor : public xgboost::Predictor {
// Add the base margin term to last column
p_fmat->Info().base_margin_.SetDevice(ctx_->gpu_id);
const auto margin = p_fmat->Info().base_margin_.Data()->ConstDeviceSpan();
float base_score = model.learner_model_param->base_score;
dh::LaunchN(
p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
[=] __device__(size_t idx) {
phis[(idx + 1) * contributions_columns - 1] +=
margin.empty() ? base_score : margin[idx];
});
auto base_score = model.learner_model_param->BaseScore(ctx_);
dh::LaunchN(p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
[=] __device__(size_t idx) {
phis[(idx + 1) * contributions_columns - 1] +=
margin.empty() ? base_score(0) : margin[idx];
});
}
void PredictInteractionContributions(DMatrix* p_fmat,
@@ -918,17 +918,17 @@ class GPUPredictor : public xgboost::Predictor {
// Add the base margin term to last column
p_fmat->Info().base_margin_.SetDevice(ctx_->gpu_id);
const auto margin = p_fmat->Info().base_margin_.Data()->ConstDeviceSpan();
float base_score = model.learner_model_param->base_score;
auto base_score = model.learner_model_param->BaseScore(ctx_);
size_t n_features = model.learner_model_param->num_feature;
dh::LaunchN(
p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
[=] __device__(size_t idx) {
size_t group = idx % ngroup;
size_t row_idx = idx / ngroup;
phis[gpu_treeshap::IndexPhiInteractions(
row_idx, ngroup, group, n_features, n_features, n_features)] +=
margin.empty() ? base_score : margin[idx];
});
dh::LaunchN(p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
[=] __device__(size_t idx) {
size_t group = idx % ngroup;
size_t row_idx = idx / ngroup;
phis[gpu_treeshap::IndexPhiInteractions(row_idx, ngroup, group, n_features,
n_features, n_features)] +=
margin.empty() ? base_score(0) : margin[idx];
});
}
void PredictInstance(const SparsePage::Inst&,