f0064c07ab
* Refactor configuration [Part II].
* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.
* Learner changes:
** Make `LearnerImpl` the only source of configuration.
All configurations are stored and carried out by `LearnerImpl::Configure()`.
** Remove booster in C API.
Originally kept for "compatibility reason", but did not state why. So here
we just remove it.
** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`. Configuration will always be lazy.
** Run `Configure` before every iteration.
* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.
`GBTree` is now used to dispatch the predictor.
** Remove some GPU Predictor tests.
* IO
No IO changes. The binary model format stability is tested by comparing
hashing value of save models between two commits
35 lines
1.0 KiB
C++
35 lines
1.0 KiB
C++
// Copyright by Contributors
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#include <xgboost/objective.h>
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#include <xgboost/generic_parameters.h>
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#include "../helpers.h"
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TEST(Objective, PairwiseRankingGPair) {
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xgboost::GenericParameter tparam;
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std::vector<std::pair<std::string, std::string>> args;
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tparam.InitAllowUnknown(args);
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xgboost::ObjFunction * obj =
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xgboost::ObjFunction::Create("rank:pairwise", &tparam);
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obj->Configure(args);
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// Test with setting sample weight to second query group
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CheckRankingObjFunction(obj,
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{0, 0.1f, 0, 0.1f},
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{0, 1, 0, 1},
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{2.0f, 0.0f},
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{0, 2, 4},
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{1.9f, -1.9f, 0.0f, 0.0f},
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{1.995f, 1.995f, 0.0f, 0.0f});
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CheckRankingObjFunction(obj,
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{0, 0.1f, 0, 0.1f},
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{0, 1, 0, 1},
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{1.0f, 1.0f},
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{0, 2, 4},
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{0.95f, -0.95f, 0.95f, -0.95f},
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{0.9975f, 0.9975f, 0.9975f, 0.9975f});
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ASSERT_NO_THROW(obj->DefaultEvalMetric());
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delete obj;
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}
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