0d0abe1845
* Implement `MaxCategory` in quantile. * Implement partition-based split for GPU evaluation. Currently, it's based on the existing evaluation function. * Extract an evaluator from GPU Hist to store the needed states. * Added some CUDA stream/event utilities. * Update document with references. * Fixed a bug in approx evaluator where the number of data points is less than the number of categories.
44 lines
1.2 KiB
C++
44 lines
1.2 KiB
C++
/*!
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* Copyright 2021-2022 by XGBoost Contributors
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*/
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#ifndef XGBOOST_TASK_H_
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#define XGBOOST_TASK_H_
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#include <cinttypes>
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namespace xgboost {
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/*!
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* \brief A struct returned by objective, which determines task at hand. The struct is
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* not used by any algorithm yet, only for future development like categorical
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* split.
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*
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* The task field is useful for tree split finding, also for some metrics like auc.
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* Lastly, knowing whether hessian is constant can allow some optimizations like skipping
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* the quantile sketching.
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*
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* This struct should not be serialized since it can be recovered from objective function,
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* hence it doesn't need to be stable.
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*/
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struct ObjInfo {
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// What kind of problem are we trying to solve
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enum Task : uint8_t {
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kRegression = 0,
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kBinary = 1,
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kClassification = 2,
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kSurvival = 3,
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kRanking = 4,
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kOther = 5,
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} task;
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// Does the objective have constant hessian value?
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bool const_hess{false};
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explicit ObjInfo(Task t) : task{t} {}
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ObjInfo(Task t, bool khess) : task{t}, const_hess{khess} {}
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constexpr bool UseOneHot() const {
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return (task != ObjInfo::kRegression && task != ObjInfo::kBinary);
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}
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};
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} // namespace xgboost
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#endif // XGBOOST_TASK_H_
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