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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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20
plugin/example/custom_obj.cc
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@@ -33,35 +33,35 @@ class MyLogistic : public ObjFunction {
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
}
void GetGradient(const std::vector<float> &preds,
void GetGradient(const std::vector<bst_float> &preds,
const MetaInfo &info,
int iter,
std::vector<bst_gpair> *out_gpair) override {
out_gpair->resize(preds.size());
for (size_t i = 0; i < preds.size(); ++i) {
float w = info.GetWeight(i);
bst_float w = info.GetWeight(i);
// scale the negative examples!
if (info.labels[i] == 0.0f) w *= param_.scale_neg_weight;
// logistic transoformation
float p = 1.0f / (1.0f + expf(-preds[i]));
// logistic transformation
bst_float p = 1.0f / (1.0f + std::exp(-preds[i]));
// this is the gradient
float grad = (p - info.labels[i]) * w;
bst_float grad = (p - info.labels[i]) * w;
// this is the second order gradient
float hess = p * (1.0f - p) * w;
bst_float hess = p * (1.0f - p) * w;
out_gpair->at(i) = bst_gpair(grad, hess);
}
}
const char* DefaultEvalMetric() const override {
return "error";
}
void PredTransform(std::vector<float> *io_preds) override {
void PredTransform(std::vector<bst_float> *io_preds) override {
// transform margin value to probability.
std::vector<float> &preds = *io_preds;
std::vector<bst_float> &preds = *io_preds;
for (size_t i = 0; i < preds.size(); ++i) {
preds[i] = 1.0f / (1.0f + expf(-preds[i]));
preds[i] = 1.0f / (1.0f + std::exp(-preds[i]));
}
}
float ProbToMargin(float base_score) const override {
bst_float ProbToMargin(bst_float base_score) const override {
// transform probability to margin value
return -std::log(1.0f / base_score - 1.0f);
}