fix for loss_type problem in outside reset base
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8e941b2a79
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@ -34,7 +34,7 @@ namespace xgboost{
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RegRankBoostLearner(const std::vector<const DMatrix *>& mats){
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silent = 0;
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obj_ = NULL;
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name_obj_ = "reg";
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name_obj_ = "reg:linear";
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this->SetCacheData(mats);
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}
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/*!
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@ -114,7 +114,7 @@ namespace xgboost{
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*/
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inline void InitModel(void){
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base_gbm.InitModel();
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mparam.AdjustBase();
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mparam.AdjustBase(name_obj_.c_str());
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}
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/*!
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* \brief load model from file
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@ -308,7 +308,7 @@ namespace xgboost{
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/*! \brief constructor */
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ModelParam(void){
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base_score = 0.5f;
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loss_type = 0;
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loss_type = -1;
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num_feature = 0;
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num_class = 0;
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memset(reserved, 0, sizeof(reserved));
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@ -320,14 +320,19 @@ namespace xgboost{
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*/
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inline void SetParam(const char *name, const char *val){
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if (!strcmp("base_score", name)) base_score = (float)atof(val);
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if (!strcmp("loss_type", name)) loss_type = atoi(val);
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if (!strcmp("num_class", name)) num_class = atoi(val);
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if (!strcmp("loss_type", name)) loss_type = atoi(val);
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if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);
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}
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/*!
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* \brief adjust base_score
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* \brief adjust base_score based on loss type and objective function
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*/
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inline void AdjustBase(void){
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inline void AdjustBase(const char *obj){
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// some tweaks for loss type
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if( loss_type == -1 ){
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loss_type = 1;
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if( !strcmp("reg:linear", obj ) ) loss_type = 0;
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}
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if (loss_type == 1 || loss_type == 2|| loss_type == 3){
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utils::Assert(base_score > 0.0f && base_score < 1.0f, "sigmoid range constrain");
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base_score = -logf(1.0f / base_score - 1.0f);
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@ -20,6 +20,7 @@ namespace xgboost{
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* \param val value of the parameter
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*/
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virtual void SetParam(const char *name, const char *val) = 0;
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/*!
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* \brief get gradient over each of predictions, given existing information
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* \param preds prediction of current round
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