Define multi-strategy parameter. (#8890)
This commit is contained in:
Jiaming Yuan
GitHub
parent
6deaec8027
commit
2aa838c75e
@ -8,29 +8,33 @@
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#ifndef XGBOOST_LEARNER_H_
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#define XGBOOST_LEARNER_H_
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#include <dmlc/io.h> // Serializable
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#include <xgboost/base.h>
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#include <xgboost/context.h> // Context
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#include <xgboost/feature_map.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/linalg.h> // Tensor
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#include <xgboost/model.h>
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#include <xgboost/task.h>
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#include <dmlc/io.h> // for Serializable
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#include <xgboost/base.h> // for bst_feature_t, bst_target_t, bst_float, Args, GradientPair
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#include <xgboost/context.h> // for Context
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#include <xgboost/linalg.h> // for Tensor, TensorView
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#include <xgboost/metric.h> // for Metric
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#include <xgboost/model.h> // for Configurable, Model
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#include <xgboost/span.h> // for Span
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#include <xgboost/task.h> // for ObjInfo
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#include <map>
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include <algorithm> // for max
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#include <cstdint> // for int32_t, uint32_t, uint8_t
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#include <map> // for map
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#include <memory> // for shared_ptr, unique_ptr
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#include <string> // for string
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#include <utility> // for move
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#include <vector> // for vector
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namespace xgboost {
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class FeatureMap;
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class Metric;
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class GradientBooster;
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class ObjFunction;
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class DMatrix;
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class Json;
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struct XGBAPIThreadLocalEntry;
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template <typename T>
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class HostDeviceVector;
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enum class PredictionType : std::uint8_t { // NOLINT
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kValue = 0,
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@ -143,7 +147,10 @@ class Learner : public Model, public Configurable, public dmlc::Serializable {
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* \brief Get number of boosted rounds from gradient booster.
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*/
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virtual int32_t BoostedRounds() const = 0;
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virtual uint32_t Groups() const = 0;
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/**
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* \brief Get the number of output groups from the model.
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*/
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virtual std::uint32_t Groups() const = 0;
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void LoadModel(Json const& in) override = 0;
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void SaveModel(Json* out) const override = 0;
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@ -275,8 +282,16 @@ class Learner : public Model, public Configurable, public dmlc::Serializable {
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struct LearnerModelParamLegacy;
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/*
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* \brief Basic Model Parameters, used to describe the booster.
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/**
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* \brief Strategy for building multi-target models.
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*/
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enum class MultiStrategy : std::int32_t {
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kComposite = 0,
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kMonolithic = 1,
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};
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/**
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* \brief Basic model parameters, used to describe the booster.
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*/
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struct LearnerModelParam {
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private:
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@ -287,30 +302,51 @@ struct LearnerModelParam {
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linalg::Tensor<float, 1> base_score_;
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public:
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/* \brief number of features */
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uint32_t num_feature { 0 };
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/* \brief number of classes, if it is multi-class classification */
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uint32_t num_output_group { 0 };
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/* \brief Current task, determined by objective. */
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/**
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* \brief The number of features.
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*/
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bst_feature_t num_feature{0};
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/**
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* \brief The number of classes or targets.
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*/
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std::uint32_t num_output_group{0};
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/**
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* \brief Current task, determined by objective.
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*/
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ObjInfo task{ObjInfo::kRegression};
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/**
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* \brief Strategy for building multi-target models.
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*/
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MultiStrategy multi_strategy{MultiStrategy::kComposite};
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LearnerModelParam() = default;
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// As the old `LearnerModelParamLegacy` is still used by binary IO, we keep
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// this one as an immutable copy.
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LearnerModelParam(Context const* ctx, LearnerModelParamLegacy const& user_param,
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linalg::Tensor<float, 1> base_margin, ObjInfo t);
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LearnerModelParam(LearnerModelParamLegacy const& user_param, ObjInfo t);
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LearnerModelParam(bst_feature_t n_features, linalg::Tensor<float, 1> base_margin,
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uint32_t n_groups)
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: base_score_{std::move(base_margin)}, num_feature{n_features}, num_output_group{n_groups} {}
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linalg::Tensor<float, 1> base_margin, ObjInfo t, MultiStrategy multi_strategy);
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LearnerModelParam(LearnerModelParamLegacy const& user_param, ObjInfo t,
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MultiStrategy multi_strategy);
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LearnerModelParam(bst_feature_t n_features, linalg::Tensor<float, 1> base_score,
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std::uint32_t n_groups, bst_target_t n_targets, MultiStrategy multi_strategy)
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: base_score_{std::move(base_score)},
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num_feature{n_features},
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num_output_group{std::max(n_groups, n_targets)},
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multi_strategy{multi_strategy} {}
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linalg::TensorView<float const, 1> BaseScore(Context const* ctx) const;
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linalg::TensorView<float const, 1> BaseScore(int32_t device) const;
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[[nodiscard]] linalg::TensorView<float const, 1> BaseScore(std::int32_t device) const;
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void Copy(LearnerModelParam const& that);
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[[nodiscard]] bool IsVectorLeaf() const noexcept {
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return multi_strategy == MultiStrategy::kMonolithic;
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}
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[[nodiscard]] bst_target_t OutputLength() const noexcept { return this->num_output_group; }
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[[nodiscard]] bst_target_t LeafLength() const noexcept {
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return this->IsVectorLeaf() ? this->OutputLength() : 1;
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}
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/* \brief Whether this parameter is initialized with LearnerModelParamLegacy. */
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bool Initialized() const { return num_feature != 0 && num_output_group != 0; }
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[[nodiscard]] bool Initialized() const { return num_feature != 0 && num_output_group != 0; }
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};
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} // namespace xgboost
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@ -12,10 +12,11 @@
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#include <vector>
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#include "xgboost/c_api.h"
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#include "xgboost/data.h" // DMatrix
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#include "xgboost/data.h" // DMatrix
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#include "xgboost/feature_map.h" // for FeatureMap
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#include "xgboost/json.h"
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#include "xgboost/learner.h"
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#include "xgboost/linalg.h" // ArrayInterfaceHandler
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#include "xgboost/linalg.h" // ArrayInterfaceHandler
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#include "xgboost/logging.h"
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#include "xgboost/string_view.h" // StringView
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177
src/learner.cc
177
src/learner.cc
@ -6,54 +6,67 @@
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*/
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#include "xgboost/learner.h"
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#include <dmlc/any.h>
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#include <dmlc/io.h>
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#include <dmlc/parameter.h>
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#include <dmlc/thread_local.h>
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#include <dmlc/io.h> // for Stream
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#include <dmlc/parameter.h> // for FieldEntry, DMLC_DECLARE_FIELD, Parameter, DMLC...
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#include <dmlc/thread_local.h> // for ThreadLocalStore
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#include <algorithm>
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#include <array>
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#include <atomic>
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#include <iomanip>
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#include <limits> // std::numeric_limits
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#include <memory>
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#include <mutex>
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#include <sstream>
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#include <stack>
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#include <string>
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#include <utility> // for as_const
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#include <vector>
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#include <algorithm> // for equal, max, transform, sort, find_if, all_of
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#include <array> // for array
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#include <atomic> // for atomic
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#include <cctype> // for isalpha, isspace
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#include <cmath> // for isnan, isinf
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#include <cstdint> // for int32_t, uint32_t, int64_t, uint64_t
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#include <cstdlib> // for atoi
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#include <cstring> // for memcpy, size_t, memset
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#include <functional> // for less
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#include <iomanip> // for operator<<, setiosflags
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#include <iterator> // for back_insert_iterator, distance, back_inserter
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#include <limits> // for numeric_limits
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#include <memory> // for allocator, unique_ptr, shared_ptr, operator==
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#include <mutex> // for mutex, lock_guard
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#include <set> // for set
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#include <sstream> // for operator<<, basic_ostream, basic_ostream::opera...
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#include <stack> // for stack
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#include <string> // for basic_string, char_traits, operator<, string
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#include <system_error> // for errc
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#include <tuple> // for get
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#include <unordered_map> // for operator!=, unordered_map
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#include <utility> // for pair, as_const, move, swap
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#include <vector> // for vector
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#include "collective/communicator-inl.h"
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#include "common/api_entry.h" // XGBAPIThreadLocalEntry
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#include "common/charconv.h"
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#include "common/common.h"
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#include "common/io.h"
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#include "common/observer.h"
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#include "common/random.h"
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#include "common/threading_utils.h"
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#include "common/timer.h"
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#include "common/version.h"
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#include "xgboost/base.h"
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#include "xgboost/c_api.h"
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#include "xgboost/context.h" // Context
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#include "xgboost/data.h"
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#include "xgboost/feature_map.h"
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#include "xgboost/gbm.h"
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#include "xgboost/host_device_vector.h"
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#include "xgboost/json.h"
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#include "xgboost/logging.h"
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#include "xgboost/metric.h"
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#include "xgboost/model.h"
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#include "xgboost/objective.h"
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#include "xgboost/parameter.h"
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#include "xgboost/predictor.h"
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#include "collective/communicator-inl.h" // for Allreduce, Broadcast, GetRank, IsDistributed
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#include "collective/communicator.h" // for Operation
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#include "common/api_entry.h" // for XGBAPIThreadLocalEntry
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#include "common/charconv.h" // for to_chars, to_chars_result, NumericLimits, from_...
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#include "common/common.h" // for ToString, Split
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#include "common/io.h" // for PeekableInStream, ReadAll, FixedSizeStream, Mem...
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#include "common/observer.h" // for TrainingObserver
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#include "common/random.h" // for GlobalRandom
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#include "common/timer.h" // for Monitor
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#include "common/version.h" // for Version
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#include "dmlc/endian.h" // for ByteSwap, DMLC_IO_NO_ENDIAN_SWAP
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#include "xgboost/base.h" // for Args, bst_float, GradientPair, bst_feature_t
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#include "xgboost/context.h" // for Context
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#include "xgboost/data.h" // for DMatrix, MetaInfo
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#include "xgboost/gbm.h" // for GradientBooster
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#include "xgboost/global_config.h" // for GlobalConfiguration, GlobalConfigThreadLocalStore
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#include "xgboost/host_device_vector.h" // for HostDeviceVector
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#include "xgboost/json.h" // for Json, get, Object, String, IsA, Array, ToJson
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#include "xgboost/linalg.h" // for Tensor, TensorView
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#include "xgboost/logging.h" // for CHECK, LOG, CHECK_EQ
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#include "xgboost/metric.h" // for Metric
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#include "xgboost/objective.h" // for ObjFunction
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#include "xgboost/parameter.h" // for DECLARE_FIELD_ENUM_CLASS, XGBoostParameter
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#include "xgboost/predictor.h" // for PredictionContainer, PredictionCacheEntry
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#include "xgboost/string_view.h" // for operator<<, StringView
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#include "xgboost/task.h" // for ObjInfo
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namespace {
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const char* kMaxDeltaStepDefaultValue = "0.7";
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} // anonymous namespace
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DECLARE_FIELD_ENUM_CLASS(xgboost::MultiStrategy);
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namespace xgboost {
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Learner::~Learner() = default;
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namespace {
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@ -86,8 +99,10 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
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/*! \brief the version of XGBoost. */
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std::uint32_t major_version;
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std::uint32_t minor_version;
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uint32_t num_target{1};
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/**
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* \brief Number of target variables.
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*/
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bst_target_t num_target;
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/**
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* \brief Whether we should calculate the base score from training data.
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*
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@ -113,7 +128,7 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
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}
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// Skip other legacy fields.
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Json ToJson() const {
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[[nodiscard]] Json ToJson() const {
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Json obj{Object{}};
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char floats[NumericLimits<float>::kToCharsSize];
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auto ret = to_chars(floats, floats + NumericLimits<float>::kToCharsSize, base_score);
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@ -163,7 +178,7 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
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from_chars(str.c_str(), str.c_str() + str.size(), base_score);
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}
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LearnerModelParamLegacy ByteSwap() const {
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[[nodiscard]] LearnerModelParamLegacy ByteSwap() const {
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LearnerModelParamLegacy x = *this;
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dmlc::ByteSwap(&x.base_score, sizeof(x.base_score), 1);
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dmlc::ByteSwap(&x.num_feature, sizeof(x.num_feature), 1);
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@ -226,35 +241,38 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
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DMLC_DECLARE_FIELD(num_feature)
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.set_default(0)
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.describe(
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"Number of features in training data,"
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" this parameter will be automatically detected by learner.");
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"Number of features in training data, this parameter will be automatically detected by "
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"learner.");
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DMLC_DECLARE_FIELD(num_class).set_default(0).set_lower_bound(0).describe(
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"Number of class option for multi-class classifier. "
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" By default equals 0 and corresponds to binary classifier.");
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DMLC_DECLARE_FIELD(num_target)
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.set_default(1)
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.set_lower_bound(1)
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.describe("Number of target for multi-target regression.");
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.describe("Number of output targets. Can be set automatically if not specified.");
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DMLC_DECLARE_FIELD(boost_from_average)
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.set_default(true)
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.describe("Whether we should calculate the base score from training data.");
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}
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};
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LearnerModelParam::LearnerModelParam(LearnerModelParamLegacy const& user_param, ObjInfo t)
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: num_feature{user_param.num_feature}, task{t} {
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auto n_classes = std::max(static_cast<uint32_t>(user_param.num_class), 1u);
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auto n_targets = user_param.num_target;
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num_output_group = std::max(n_classes, n_targets);
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// For version < 1.6, n_targets == 0
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CHECK(n_classes <= 1 || n_targets <= 1)
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<< "Multi-class multi-output is not yet supported. n_classes:" << n_classes
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<< ", n_targets:" << n_targets;
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LearnerModelParam::LearnerModelParam(LearnerModelParamLegacy const& user_param, ObjInfo t,
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MultiStrategy multi_strategy)
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: num_feature{user_param.num_feature},
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num_output_group{
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std::max(static_cast<std::uint32_t>(user_param.num_class), user_param.num_target)},
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task{t},
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multi_strategy{multi_strategy} {
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if (user_param.num_class > 1 && user_param.num_target > 1) {
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LOG(FATAL) << "multi-target-multi-class is not yet supported. Output classes:"
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<< user_param.num_class << ", output targets:" << user_param.num_target;
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}
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}
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LearnerModelParam::LearnerModelParam(Context const* ctx, LearnerModelParamLegacy const& user_param,
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linalg::Tensor<float, 1> base_margin, ObjInfo t)
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: LearnerModelParam{user_param, t} {
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linalg::Tensor<float, 1> base_margin, ObjInfo t,
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MultiStrategy multi_strategy)
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: LearnerModelParam{user_param, t, multi_strategy} {
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std::swap(base_score_, base_margin);
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// Make sure read access everywhere for thread-safe prediction.
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std::as_const(base_score_).HostView();
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@ -297,6 +315,7 @@ void LearnerModelParam::Copy(LearnerModelParam const& that) {
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num_feature = that.num_feature;
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num_output_group = that.num_output_group;
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task = that.task;
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multi_strategy = that.multi_strategy;
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}
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struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
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@ -306,18 +325,26 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
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// specified by users. Move them to model parameter once we can get rid of binary IO.
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std::string booster;
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std::string objective;
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// This is a training parameter and is not saved (nor loaded) in the model.
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MultiStrategy multi_strategy{MultiStrategy::kComposite};
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// declare parameters
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DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
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DMLC_DECLARE_FIELD(disable_default_eval_metric)
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.set_default(false)
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.describe("Flag to disable default metric. Set to >0 to disable");
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DMLC_DECLARE_FIELD(booster)
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.set_default("gbtree")
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.describe("Gradient booster used for training.");
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DMLC_DECLARE_FIELD(booster).set_default("gbtree").describe(
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"Gradient booster used for training.");
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DMLC_DECLARE_FIELD(objective)
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.set_default("reg:squarederror")
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.describe("Objective function used for obtaining gradient.");
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DMLC_DECLARE_FIELD(multi_strategy)
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.add_enum("composite", MultiStrategy::kComposite)
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.add_enum("monolithic", MultiStrategy::kMonolithic)
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.set_default(MultiStrategy::kComposite)
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.describe(
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"Strategy used for training multi-target models. `mono` means building one single tree "
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"for all targets.");
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}
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};
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@ -379,8 +406,10 @@ class LearnerConfiguration : public Learner {
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// transform to margin
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h_base_score(0) = obj_->ProbToMargin(mparam_.base_score);
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CHECK(tparam_.GetInitialised());
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// move it to model param, which is shared with all other components.
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learner_model_param_ = LearnerModelParam(Ctx(), mparam_, std::move(base_score), task);
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learner_model_param_ =
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LearnerModelParam(Ctx(), mparam_, std::move(base_score), task, tparam_.multi_strategy);
|
||||
CHECK(learner_model_param_.Initialized());
|
||||
CHECK_NE(learner_model_param_.BaseScore(Ctx()).Size(), 0);
|
||||
}
|
||||
@ -748,7 +777,6 @@ class LearnerConfiguration : public Learner {
|
||||
<< "0 feature is supplied. Are you using raw Booster interface?";
|
||||
// Remove these once binary IO is gone.
|
||||
cfg_["num_feature"] = common::ToString(mparam_.num_feature);
|
||||
cfg_["num_class"] = common::ToString(mparam_.num_class);
|
||||
}
|
||||
|
||||
void ConfigureGBM(LearnerTrainParam const& old, Args const& args) {
|
||||
@ -779,9 +807,17 @@ class LearnerConfiguration : public Learner {
|
||||
if (obj_ == nullptr || tparam_.objective != old.objective) {
|
||||
obj_.reset(ObjFunction::Create(tparam_.objective, &ctx_));
|
||||
}
|
||||
|
||||
bool has_nc {cfg_.find("num_class") != cfg_.cend()};
|
||||
// Inject num_class into configuration.
|
||||
// FIXME(jiamingy): Remove the duplicated parameter in softmax
|
||||
cfg_["num_class"] = common::ToString(mparam_.num_class);
|
||||
auto& args = *p_args;
|
||||
args = {cfg_.cbegin(), cfg_.cend()}; // renew
|
||||
obj_->Configure(args);
|
||||
if (!has_nc) {
|
||||
cfg_.erase("num_class");
|
||||
}
|
||||
}
|
||||
|
||||
void ConfigureMetrics(Args const& args) {
|
||||
@ -805,7 +841,7 @@ class LearnerConfiguration : public Learner {
|
||||
void ConfigureTargets() {
|
||||
CHECK(this->obj_);
|
||||
auto const& cache = prediction_container_.Container();
|
||||
size_t n_targets = 1;
|
||||
bst_target_t n_targets = 1;
|
||||
for (auto const& d : cache) {
|
||||
if (n_targets == 1) {
|
||||
n_targets = this->obj_->Targets(d.first.ptr->Info());
|
||||
@ -814,7 +850,8 @@ class LearnerConfiguration : public Learner {
|
||||
CHECK(n_targets == t || 1 == t) << "Inconsistent labels.";
|
||||
}
|
||||
}
|
||||
if (mparam_.num_target != 1) {
|
||||
|
||||
if (mparam_.num_target > 1) {
|
||||
CHECK(n_targets == 1 || n_targets == mparam_.num_target)
|
||||
<< "Inconsistent configuration of num_target. Configuration result from input data:"
|
||||
<< n_targets << ", configuration from parameter:" << mparam_.num_target;
|
||||
@ -974,9 +1011,6 @@ class LearnerIO : public LearnerConfiguration {
|
||||
if (!DMLC_IO_NO_ENDIAN_SWAP) {
|
||||
mparam_ = mparam_.ByteSwap();
|
||||
}
|
||||
if (mparam_.num_target == 0) {
|
||||
mparam_.num_target = 1;
|
||||
}
|
||||
CHECK(fi->Read(&tparam_.objective)) << "BoostLearner: wrong model format";
|
||||
CHECK(fi->Read(&tparam_.booster)) << "BoostLearner: wrong model format";
|
||||
|
||||
@ -1030,7 +1064,7 @@ class LearnerIO : public LearnerConfiguration {
|
||||
: obj_->ProbToMargin(mparam_.base_score)},
|
||||
{1},
|
||||
Context::kCpuId},
|
||||
obj_->Task());
|
||||
obj_->Task(), tparam_.multi_strategy);
|
||||
|
||||
if (attributes_.find("objective") != attributes_.cend()) {
|
||||
auto obj_str = attributes_.at("objective");
|
||||
@ -1058,7 +1092,6 @@ class LearnerIO : public LearnerConfiguration {
|
||||
mparam_.major_version = std::get<0>(Version::Self());
|
||||
mparam_.minor_version = std::get<1>(Version::Self());
|
||||
|
||||
cfg_["num_class"] = common::ToString(mparam_.num_class);
|
||||
cfg_["num_feature"] = common::ToString(mparam_.num_feature);
|
||||
|
||||
auto n = tparam_.__DICT__();
|
||||
@ -1071,6 +1104,8 @@ class LearnerIO : public LearnerConfiguration {
|
||||
// JSON serialization format.
|
||||
void SaveModel(dmlc::Stream* fo) const override {
|
||||
this->CheckModelInitialized();
|
||||
CHECK(!this->learner_model_param_.IsVectorLeaf())
|
||||
<< "Please use JSON/UBJ format for model serialization with multi-output models.";
|
||||
|
||||
LearnerModelParamLegacy mparam = mparam_; // make a copy to potentially modify
|
||||
std::vector<std::pair<std::string, std::string> > extra_attr;
|
||||
|
||||
@ -3,18 +3,19 @@
|
||||
*/
|
||||
#include "xgboost/predictor.h"
|
||||
|
||||
#include <dmlc/registry.h>
|
||||
#include <dmlc/registry.h> // for DMLC_REGISTRY_LINK_TAG
|
||||
|
||||
#include <string> // std::string
|
||||
#include <cstdint> // for int32_t
|
||||
#include <string> // for string, to_string
|
||||
|
||||
#include "../gbm/gbtree.h" // GBTreeModel
|
||||
#include "xgboost/base.h" // bst_row_t,bst_group_t
|
||||
#include "xgboost/context.h" // Context
|
||||
#include "xgboost/data.h" // MetaInfo
|
||||
#include "xgboost/host_device_vector.h" // HostDeviceVector
|
||||
#include "xgboost/learner.h" // LearnerModelParam
|
||||
#include "xgboost/linalg.h" // Tensor
|
||||
#include "xgboost/logging.h"
|
||||
#include "../gbm/gbtree_model.h" // for GBTreeModel
|
||||
#include "xgboost/base.h" // for bst_float, Args, bst_group_t, bst_row_t
|
||||
#include "xgboost/context.h" // for Context
|
||||
#include "xgboost/data.h" // for MetaInfo
|
||||
#include "xgboost/host_device_vector.h" // for HostDeviceVector
|
||||
#include "xgboost/learner.h" // for LearnerModelParam
|
||||
#include "xgboost/linalg.h" // for Tensor, TensorView
|
||||
#include "xgboost/logging.h" // for CHECK_EQ, CHECK_NE, LOG
|
||||
|
||||
namespace dmlc {
|
||||
DMLC_REGISTRY_ENABLE(::xgboost::PredictorReg);
|
||||
@ -45,15 +46,16 @@ void ValidateBaseMarginShape(linalg::Tensor<float, D> const& margin, bst_row_t n
|
||||
void Predictor::InitOutPredictions(const MetaInfo& info, HostDeviceVector<bst_float>* out_preds,
|
||||
const gbm::GBTreeModel& model) const {
|
||||
CHECK_NE(model.learner_model_param->num_output_group, 0);
|
||||
size_t n_classes = model.learner_model_param->num_output_group;
|
||||
size_t n = n_classes * info.num_row_;
|
||||
std::size_t n{model.learner_model_param->OutputLength() * info.num_row_};
|
||||
|
||||
const HostDeviceVector<bst_float>* base_margin = info.base_margin_.Data();
|
||||
if (ctx_->gpu_id >= 0) {
|
||||
out_preds->SetDevice(ctx_->gpu_id);
|
||||
}
|
||||
if (!base_margin->Empty()) {
|
||||
out_preds->Resize(n);
|
||||
ValidateBaseMarginShape(info.base_margin_, info.num_row_, n_classes);
|
||||
ValidateBaseMarginShape(info.base_margin_, info.num_row_,
|
||||
model.learner_model_param->OutputLength());
|
||||
out_preds->Copy(*base_margin);
|
||||
} else {
|
||||
// cannot rely on the Resize to fill as it might skip if the size is already correct.
|
||||
@ -64,12 +66,10 @@ void Predictor::InitOutPredictions(const MetaInfo& info, HostDeviceVector<bst_fl
|
||||
}
|
||||
} // namespace xgboost
|
||||
|
||||
namespace xgboost {
|
||||
namespace predictor {
|
||||
namespace xgboost::predictor {
|
||||
// List of files that will be force linked in static links.
|
||||
#ifdef XGBOOST_USE_CUDA
|
||||
DMLC_REGISTRY_LINK_TAG(gpu_predictor);
|
||||
#endif // XGBOOST_USE_CUDA
|
||||
DMLC_REGISTRY_LINK_TAG(cpu_predictor);
|
||||
} // namespace predictor
|
||||
} // namespace xgboost
|
||||
} // namespace xgboost::predictor
|
||||
|
||||
@ -24,6 +24,7 @@
|
||||
#include "../../src/data/array_interface.h"
|
||||
#include "../../src/gbm/gbtree_model.h"
|
||||
#include "filesystem.h" // dmlc::TemporaryDirectory
|
||||
#include "xgboost/linalg.h"
|
||||
|
||||
#if defined(__CUDACC__)
|
||||
#define DeclareUnifiedTest(name) GPU ## name
|
||||
@ -461,7 +462,7 @@ inline LearnerModelParam MakeMP(bst_feature_t n_features, float base_score, uint
|
||||
int32_t device = Context::kCpuId) {
|
||||
size_t shape[1]{1};
|
||||
LearnerModelParam mparam(n_features, linalg::Tensor<float, 1>{{base_score}, shape, device},
|
||||
n_groups);
|
||||
n_groups, 1, MultiStrategy::kComposite);
|
||||
return mparam;
|
||||
}
|
||||
|
||||
|
||||
@ -2,24 +2,26 @@
|
||||
* Copyright 2023 by XGBoost Contributors
|
||||
*/
|
||||
#include <gtest/gtest.h>
|
||||
#include <xgboost/base.h> // bst_target_t
|
||||
#include <xgboost/data.h> // DMatrix
|
||||
#include <xgboost/json.h> // Json,Object,Number,get
|
||||
#include <xgboost/learner.h> // Learner
|
||||
#include <xgboost/base.h> // for Args, bst_target_t
|
||||
#include <xgboost/data.h> // for DMatrix, MetaInfo
|
||||
#include <xgboost/json.h> // for Json, get, Object, String
|
||||
#include <xgboost/learner.h> // for Learner
|
||||
|
||||
#include <cstddef> // size_t
|
||||
#include <memory> // shared_ptr,unique_ptr
|
||||
#include <numeric>
|
||||
#include <string> // stod
|
||||
#include <vector>
|
||||
#include <algorithm> // for copy
|
||||
#include <cstddef> // for size_t
|
||||
#include <memory> // for shared_ptr, allocator, __shared_ptr_access
|
||||
#include <numeric> // for accumulate
|
||||
#include <string> // for stod, string
|
||||
#include <vector> // for vector
|
||||
|
||||
#include "../../src/common/linalg_op.h" // cbegin,cend
|
||||
#include "../../src/common/stats.h" // Median
|
||||
#include "helpers.h" // RandomDataGenerator
|
||||
#include "xgboost/linalg.h"
|
||||
#include "../../src/common/linalg_op.h" // for begin, cbegin, cend
|
||||
#include "../../src/common/stats.h" // for Median
|
||||
#include "../../src/common/transform_iterator.h" // for IndexTransformIter
|
||||
#include "helpers.h" // for RandomDataGenerator
|
||||
#include "xgboost/host_device_vector.h" // for HostDeviceVector
|
||||
#include "xgboost/linalg.h" // for Tensor, All, TensorView, Vector
|
||||
|
||||
namespace xgboost {
|
||||
|
||||
class TestL1MultiTarget : public ::testing::Test {
|
||||
std::shared_ptr<DMatrix> Xy_;
|
||||
std::shared_ptr<DMatrix> Xyw_;
|
||||
@ -117,4 +119,16 @@ TEST_F(TestL1MultiTarget, Approx) { this->RunTest("approx"); }
|
||||
#if defined(XGBOOST_USE_CUDA)
|
||||
TEST_F(TestL1MultiTarget, GpuHist) { this->RunTest("gpu_hist"); }
|
||||
#endif // defined(XGBOOST_USE_CUDA)
|
||||
|
||||
TEST(MultiStrategy, Configure) {
|
||||
auto p_fmat = RandomDataGenerator{12ul, 3ul, 0.0}.GenerateDMatrix();
|
||||
p_fmat->Info().labels.Reshape(p_fmat->Info().num_row_, 2);
|
||||
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
|
||||
learner->SetParams(Args{{"multi_strategy", "monolithic"}, {"num_target", "2"}});
|
||||
learner->Configure();
|
||||
ASSERT_EQ(learner->Groups(), 2);
|
||||
|
||||
learner->SetParams(Args{{"multi_strategy", "monolithic"}, {"num_target", "0"}});
|
||||
ASSERT_THROW({ learner->Configure(); }, dmlc::Error);
|
||||
}
|
||||
} // namespace xgboost
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user