253 lines
9.1 KiB
C++
253 lines
9.1 KiB
C++
/*!
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* Copyright 2015-2020 by Contributors
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* \file learner.h
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* \brief Learner interface that integrates objective, gbm and evaluation together.
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* This is the user facing XGBoost training module.
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* \author Tianqi Chen
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*/
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#ifndef XGBOOST_LEARNER_H_
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#define XGBOOST_LEARNER_H_
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#include <dmlc/any.h>
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#include <rabit/rabit.h>
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#include <xgboost/base.h>
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#include <xgboost/feature_map.h>
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#include <xgboost/predictor.h>
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#include <xgboost/generic_parameters.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/model.h>
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#include <utility>
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#include <map>
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#include <memory>
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#include <string>
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#include <vector>
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namespace xgboost {
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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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/*! \brief entry to to easily hold returning information */
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struct XGBAPIThreadLocalEntry {
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/*! \brief result holder for returning string */
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std::string ret_str;
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/*! \brief result holder for returning strings */
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std::vector<std::string> ret_vec_str;
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/*! \brief result holder for returning string pointers */
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std::vector<const char *> ret_vec_charp;
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/*! \brief returning float vector. */
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std::vector<bst_float> ret_vec_float;
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/*! \brief temp variable of gradient pairs. */
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std::vector<GradientPair> tmp_gpair;
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PredictionCacheEntry prediction_entry;
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};
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/*!
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* \brief Learner class that does training and prediction.
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* This is the user facing module of xgboost training.
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* The Load/Save function corresponds to the model used in python/R.
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* \code
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*
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* std::unique_ptr<Learner> learner(new Learner::Create(cache_mats));
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* learner.Configure(configs);
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*
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* for (int iter = 0; iter < max_iter; ++iter) {
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* learner->UpdateOneIter(iter, train_mat);
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* LOG(INFO) << learner->EvalOneIter(iter, data_sets, data_names);
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* }
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*
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* \endcode
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*/
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class Learner : public Model, public Configurable, public rabit::Serializable {
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public:
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/*! \brief virtual destructor */
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~Learner() override;
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/*!
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* \brief Configure Learner based on set parameters.
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*/
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virtual void Configure() = 0;
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/*!
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* \brief update the model for one iteration
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* With the specified objective function.
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* \param iter current iteration number
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* \param train reference to the data matrix.
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*/
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virtual void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train) = 0;
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/*!
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* \brief Do customized gradient boosting with in_gpair.
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* in_gair can be mutated after this call.
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* \param iter current iteration number
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* \param train reference to the data matrix.
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* \param in_gpair The input gradient statistics.
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*/
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virtual void BoostOneIter(int iter,
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std::shared_ptr<DMatrix> train,
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HostDeviceVector<GradientPair>* in_gpair) = 0;
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/*!
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* \brief evaluate the model for specific iteration using the configured metrics.
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* \param iter iteration number
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* \param data_sets datasets to be evaluated.
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* \param data_names name of each dataset
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* \return a string corresponding to the evaluation result
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*/
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virtual std::string EvalOneIter(int iter,
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const std::vector<std::shared_ptr<DMatrix>>& data_sets,
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const std::vector<std::string>& data_names) = 0;
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/*!
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* \brief get prediction given the model.
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* \param data input data
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* \param output_margin whether to only predict margin value instead of transformed prediction
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* \param out_preds output vector that stores the prediction
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* \param ntree_limit limit number of trees used for boosted tree
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* predictor, when it equals 0, this means we are using all the trees
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* \param training Whether the prediction result is used for training
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* \param pred_leaf whether to only predict the leaf index of each tree in a boosted tree predictor
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* \param pred_contribs whether to only predict the feature contributions
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* \param approx_contribs whether to approximate the feature contributions for speed
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* \param pred_interactions whether to compute the feature pair contributions
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*/
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virtual void Predict(std::shared_ptr<DMatrix> data,
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bool output_margin,
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HostDeviceVector<bst_float> *out_preds,
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unsigned ntree_limit = 0,
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bool training = false,
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bool pred_leaf = false,
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bool pred_contribs = false,
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bool approx_contribs = false,
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bool pred_interactions = false) = 0;
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/*!
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* \brief Inplace prediction.
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*
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* \param x A type erased data adapter.
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* \param type Prediction type.
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* \param missing Missing value in the data.
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* \param [in,out] out_preds Pointer to output prediction vector.
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* \param layer_begin (Optional) Begining of boosted tree layer used for prediction.
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* \param layer_end (Optional) End of booster layer. 0 means do not limit trees.
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*/
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virtual void InplacePredict(dmlc::any const& x, std::string const& type,
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float missing,
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HostDeviceVector<bst_float> **out_preds,
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uint32_t layer_begin = 0, uint32_t layer_end = 0) = 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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virtual void LoadModel(dmlc::Stream* fi) = 0;
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virtual void SaveModel(dmlc::Stream* fo) const = 0;
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/*!
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* \brief Set multiple parameters at once.
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*
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* \param args parameters.
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*/
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virtual void SetParams(Args const& args) = 0;
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/*!
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* \brief Set parameter for booster
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*
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* The property will NOT be saved along with booster
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*
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* \param key The key of parameter
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* \param value The value of parameter
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*/
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virtual void SetParam(const std::string& key, const std::string& value) = 0;
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/*!
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* \brief Set additional attribute to the Booster.
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*
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* The property will be saved along the booster.
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*
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* \param key The key of the property.
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* \param value The value of the property.
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*/
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virtual void SetAttr(const std::string& key, const std::string& value) = 0;
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/*!
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* \brief Get attribute from the booster.
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* The property will be saved along the booster.
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* \param key The key of the attribute.
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* \param out The output value.
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* \return Whether the key exists among booster's attributes.
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*/
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virtual bool GetAttr(const std::string& key, std::string* out) const = 0;
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/*!
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* \brief Delete an attribute from the booster.
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* \param key The key of the attribute.
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* \return Whether the key was found among booster's attributes.
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*/
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virtual bool DelAttr(const std::string& key) = 0;
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/*!
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* \brief Get a vector of attribute names from the booster.
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* \return vector of attribute name strings.
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*/
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virtual std::vector<std::string> GetAttrNames() const = 0;
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/*!
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* \return whether the model allow lazy checkpoint in rabit.
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*/
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bool AllowLazyCheckPoint() const;
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/*!
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* \brief dump the model in the requested format
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* \param fmap feature map that may help give interpretations of feature
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* \param with_stats extra statistics while dumping model
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* \param format the format to dump the model in
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* \return a vector of dump for boosters.
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*/
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virtual std::vector<std::string> DumpModel(const FeatureMap& fmap,
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bool with_stats,
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std::string format) = 0;
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virtual XGBAPIThreadLocalEntry& GetThreadLocal() const = 0;
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/*!
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* \brief Create a new instance of learner.
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* \param cache_data The matrix to cache the prediction.
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* \return Created learner.
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*/
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static Learner* Create(const std::vector<std::shared_ptr<DMatrix> >& cache_data);
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virtual GenericParameter const& GetGenericParameter() const = 0;
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/*!
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* \brief Get configuration arguments currently stored by the learner
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* \return Key-value pairs representing configuration arguments
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*/
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virtual const std::map<std::string, std::string>& GetConfigurationArguments() const = 0;
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protected:
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/*! \brief objective function */
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std::unique_ptr<ObjFunction> obj_;
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/*! \brief The gradient booster used by the model*/
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std::unique_ptr<GradientBooster> gbm_;
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/*! \brief The evaluation metrics used to evaluate the model. */
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std::vector<std::unique_ptr<Metric> > metrics_;
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/*! \brief Training parameter. */
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GenericParameter generic_parameters_;
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};
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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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struct LearnerModelParam {
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/* \brief global bias */
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bst_float base_score { 0.5f };
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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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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(LearnerModelParamLegacy const& user_param, float base_margin);
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/* \brief Whether this parameter is initialized with LearnerModelParamLegacy. */
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bool Initialized() const { return num_feature != 0; }
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};
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} // namespace xgboost
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#endif // XGBOOST_LEARNER_H_
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