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add in grad and hess rescale in lambdarank

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This commit is contained in:
kalenhaha 2014-05-14 23:13:27 +08:00
parent 671c34be63
commit 121348c0d7
7 changed files with 14 additions and 30 deletions
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5
demo/rank/toy.eval
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demo/rank/toy.eval.group
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demo/rank/toy.test
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demo/rank/toy.test.group
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demo/rank/toy.train
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demo/rank/toy.train.group
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regrank/xgboost_regrank_obj.hpp
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@ -280,11 +280,13 @@ namespace xgboost{
virtual ~LambdaRankObj(){}
virtual void SetParam(const char *name, const char *val){
if (!strcmp("loss_type", name)) loss_.loss_type = atoi(val);
}
if( !strcmp( "loss_type", name ) ) loss_.loss_type = atoi( val );
if( !strcmp( "fix_list_weight", name ) ) fix_list_weight_ = (float)atof( val );
}
private:
LossType loss_;
float fix_list_weight_;
protected:
class Triple{
@ -372,6 +374,15 @@ namespace xgboost{
grad[pairs[i].first] += first_order_gradient;
hess[pairs[i].second] += second_order_gradient;
grad[pairs[i].second] -= first_order_gradient;
}
if( fix_list_weight_ != 0.0f ){
float scale = fix_list_weight_ / (group_index[group+1] - group_index[group]);
for(unsigned j = group_index[group]; j < group_index[group+1]; ++j ){
grad[j] *= scale;
hess[j] *= scale;
}
}
}