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Use bst_float consistently throughout (#1824)

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* Fix various typos

* Add override to functions that are overridden

gcc gives warnings about functions that are being overridden by not
being marked as oveirridden. This fixes it.

* Use bst_float consistently

Use bst_float for all the variables that involve weight,
leaf value, gradient, hessian, gain, loss_chg, predictions,
base_margin, feature values.

In some cases, when due to additions and so on the value can
take a larger value, double is used.

This ensures that type conversions are minimal and reduces loss of
precision.
This commit is contained in:
AbdealiJK
2016-11-30 23:32:10 +05:30
committed by Tianqi Chen
parent da2556f58a
commit 6f16f0ef58
50 changed files with 392 additions and 389 deletions
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12
src/learner.cc
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@@ -36,7 +36,7 @@ Learner::DumpModel(const FeatureMap& fmap,
struct LearnerModelParam
: public dmlc::Parameter<LearnerModelParam> {
/* \brief global bias */
float base_score;
bst_float base_score;
/* \brief number of features */
unsigned num_feature;
/* \brief number of classes, if it is multi-class classification */
@@ -353,7 +353,7 @@ class LearnerImpl : public Learner {
return out;
}
std::pair<std::string, float> Evaluate(DMatrix* data, std::string metric) {
std::pair<std::string, bst_float> Evaluate(DMatrix* data, std::string metric) {
if (metric == "auto") metric = obj_->DefaultEvalMetric();
std::unique_ptr<Metric> ev(Metric::Create(metric.c_str()));
this->PredictRaw(data, &preds_);
@@ -363,7 +363,7 @@ class LearnerImpl : public Learner {
void Predict(DMatrix* data,
bool output_margin,
std::vector<float> *out_preds,
std::vector<bst_float> *out_preds,
unsigned ntree_limit,
bool pred_leaf) const override {
if (pred_leaf) {
@@ -460,7 +460,7 @@ class LearnerImpl : public Learner {
* predictor, when it equals 0, this means we are using all the trees
*/
inline void PredictRaw(DMatrix* data,
std::vector<float>* out_preds,
std::vector<bst_float>* out_preds,
unsigned ntree_limit = 0) const {
CHECK(gbm_.get() != nullptr)
<< "Predict must happen after Load or InitModel";
@@ -478,10 +478,10 @@ class LearnerImpl : public Learner {
std::map<std::string, std::string> attributes_;
// name of gbm
std::string name_gbm_;
// name of objective functon
// name of objective function
std::string name_obj_;
// temporal storages for prediction
std::vector<float> preds_;
std::vector<bst_float> preds_;
// gradient pairs
std::vector<bst_gpair> gpair_;