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add object bound checking

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This commit is contained in:
tqchen 2014-09-08 16:20:41 -07:00
parent df3eafc5ba
commit e90b25a381
1 changed files with 23 additions and 0 deletions
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23
src/learner/objective-inl.hpp
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@ -41,6 +41,25 @@ struct LossType {
default: utils::Error("unknown loss_type"); return 0.0f; default: utils::Error("unknown loss_type"); return 0.0f;
} }
} }
/*!
* \brief check if label range is valid
*/
inline bool CheckLabel(float x) const {
if (loss_type != kLinearSquare) {
return x >= 0.0f && x <= 1.0f;
}
return true;
}
/*!
* \brief error message displayed when check label fail
*/
inline const char * CheckLabelErrorMsg(void) const {
if (loss_type != kLinearSquare) {
return "label must be in [0,1] for logistic regression";
} else {
return "";
}
}
/*! /*!
* \brief calculate first order gradient of loss, given transformed prediction * \brief calculate first order gradient of loss, given transformed prediction
* \param predt transformed prediction * \param predt transformed prediction
@ -115,6 +134,8 @@ class RegLossObj : public IObjFunction{
"labels are not correctly provided"); "labels are not correctly provided");
std::vector<bst_gpair> &gpair = *out_gpair; std::vector<bst_gpair> &gpair = *out_gpair;
gpair.resize(preds.size()); gpair.resize(preds.size());
// check if label in range
bool label_correct = true;
// start calculating gradient // start calculating gradient
const unsigned nstep = static_cast<unsigned>(info.labels.size()); const unsigned nstep = static_cast<unsigned>(info.labels.size());
const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size()); const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
@ -124,9 +145,11 @@ class RegLossObj : public IObjFunction{
float p = loss.PredTransform(preds[i]); float p = loss.PredTransform(preds[i]);
float w = info.GetWeight(j); float w = info.GetWeight(j);
if (info.labels[j] == 1.0f) w *= scale_pos_weight; if (info.labels[j] == 1.0f) w *= scale_pos_weight;
if (!loss.CheckLabel(info.labels[j])) label_correct = false;
gpair[i] = bst_gpair(loss.FirstOrderGradient(p, info.labels[j]) * w, gpair[i] = bst_gpair(loss.FirstOrderGradient(p, info.labels[j]) * w,
loss.SecondOrderGradient(p, info.labels[j]) * w); loss.SecondOrderGradient(p, info.labels[j]) * w);
} }
utils::Check(label_correct, loss.CheckLabelErrorMsg());
} }
virtual const char* DefaultEvalMetric(void) const { virtual const char* DefaultEvalMetric(void) const {
return loss.DefaultEvalMetric(); return loss.DefaultEvalMetric();