initial work for pairwise ranking (dataset part)

include/LightGBM/config.h

@@ -36,6 +36,11 @@ enum TaskType {
 };
 const int kDefaultNumLeaves = 31;
 
+/*! \brief Types of pairwise ranking mode */
+enum PairwiseRankingMode {
+  kNone, kFull, kRelevance, kManual
+};
+
 struct Config {
  public:
   std::string ToString() const;

include/LightGBM/dataset.h

@@ -199,6 +199,12 @@ class Metadata {
     const double* init_scores,
     const int32_t* queries);
 
+  /*!
+  * \brief Build metadata for ranking with pairwise features from metadata of an existing ranking dataset
+  * \param metadata Pointer to metadata of the existing ranking dataset
+  */
+  void BuildPairwiseFeatureRanking(const Metadata& metadata);
+
   /*!
   * \brief Perform any extra operations after all data has been loaded
   */
@@ -247,6 +253,18 @@ class Metadata {
       return position_ids_.size();
   }
 
+  /*!
+  * \brief Get the pairwise item index map in ranking with pairwise features
+  * \return Pointer to the pairwise item index map
+  */
+  inline const std::pair<data_size_t, data_size_t>* paired_ranking_item_index_map() const {
+    if (!paired_ranking_item_index_map_.empty()) {
+      return paired_ranking_item_index_map_.data();
+    } else {
+      return nullptr;
+    }
+  }
+
   /*!
   * \brief Get data boundaries on queries, if not exists, will return nullptr
   *        we assume data will order by query,
@@ -367,6 +385,10 @@ class Metadata {
   std::vector<double> init_score_;
   /*! \brief Queries data */
   std::vector<data_size_t> queries_;
+  /*! \brief Mode for pairwise ranking */
+  PairwiseRankingMode pairwise_ranking_mode_;
+  /*! \brief Pairwise data index to original data indices for ranking with pairwise features  */
+  std::vector<std::pair<data_size_t, data_size_t>> paired_ranking_item_index_map_;
   /*! \brief mutex for threading safe call */
   std::mutex mutex_;
   bool weight_load_from_file_;
@@ -677,6 +699,8 @@ class Dataset {
 
   LIGHTGBM_EXPORT void CreateValid(const Dataset* dataset);
 
+  LIGHTGBM_EXPORT void CreatePairWiseRankingData(const Dataset* dataset, std::vector<std::pair<data_size_t, data_size_t>> pair_index_map);
+
   void InitTrain(const std::vector<int8_t>& is_feature_used,
                  TrainingShareStates* share_state) const;
 

include/LightGBM/feature_group.h

@@ -581,7 +581,7 @@ class FeatureGroup {
     }
   }
 
- private:
+ protected:
   void CreateBinData(int num_data, bool is_multi_val, bool force_dense, bool force_sparse) {
     if (is_multi_val) {
       multi_bin_data_.clear();

include/LightGBM/pairwise_ranking_feature_group.h

@@ -0,0 +1,107 @@
+/*!
+ * Copyright (c) 2023 Microsoft Corporation. All rights reserved.
+ * Licensed under the MIT License. See LICENSE file in the project root for
+ * license information.
+ */
+#ifndef LIGHTGBM_PAIRWISE_FEATURE_GROUP_H_
+#define LIGHTGBM_PAIRWISE_FEATURE_GROUP_H_
+
+#include "feature_group.h"
+
+#include <cstdio>
+#include <memory>
+#include <utility>
+#include <vector>
+
+namespace LightGBM {
+
+/*! \brief Using to store data and providing some operations on one feature
+group*/
+class PairwiseRankingFeatureGroup: public FeatureGroup {
+ public:
+  /*!
+  * \brief Constructor
+  * \param num_feature number of features of this group
+  * \param bin_mappers Bin mapper for features
+  * \param num_data Total number of data
+  * \param is_enable_sparse True if enable sparse feature
+  */
+
+  PairwiseRankingFeatureGroup(const FeatureGroup& other, int num_data) {
+    num_feature_ = other.num_feature_;
+    is_multi_val_ = false;
+    is_dense_multi_val_ = false;
+    is_sparse_ = false;
+    num_total_bin_ = other.num_total_bin_;
+    bin_offsets_ = other.bin_offsets_;
+    num_data_ = num_data;
+
+    bin_mappers_.reserve(other.bin_mappers_.size());
+    for (auto& bin_mapper : other.bin_mappers_) {
+      bin_mappers_.emplace_back(new BinMapper(*bin_mapper));
+    }
+    CreateBinData(num_data, is_multi_val_, !is_sparse_, is_sparse_);
+  }
+
+  /*!
+   * \brief Constructor from memory when data is present
+   * \param memory Pointer of memory
+   * \param num_all_data Number of global data
+   * \param local_used_indices Local used indices, empty means using all data
+   * \param group_id Id of group
+   */
+  PairwiseRankingFeatureGroup(const void* memory,
+                              data_size_t num_all_data,
+                              const std::vector<data_size_t>& local_used_indices,
+                              int group_id) {
+    // TODO(shiyu1994)
+  }
+
+  /*!
+   * \brief Constructor from definition in memory (without data)
+   * \param memory Pointer of memory
+   * \param local_used_indices Local used indices, empty means using all data
+   */
+  FeatureGroup(const void* memory, data_size_t num_data, int group_id) {
+    // TODO(shiyu1994)
+  }
+
+  /*! \brief Destructor */
+  ~PairwiseRankingFeatureGroup() {}
+
+  /*!
+   * \brief Load the overall definition of the feature group from binary serialized data
+   * \param memory Pointer of memory
+   * \param group_id Id of group
+   */
+  const char* LoadDefinitionFromMemory(const void* memory, int group_id) {
+    // TODO(shiyu1994)
+  }
+
+  inline BinIterator* SubFeatureIterator(int sub_feature) {
+    // TODO(shiyu1994)
+  }
+
+  inline void FinishLoad() {
+    // TODO(shiyu1994)
+  }
+
+  inline BinIterator* FeatureGroupIterator() {
+    // TODO(shiyu1994)
+  }
+
+ private:
+  void CreateBinData(int num_data, bool is_multi_val, bool force_dense, bool force_sparse) {
+    // TODO(shiyu1994)
+  }
+
+
+  /*! \brief Pairwise data index to original data indices for ranking with pairwise features  */
+  const std::pair<data_size_t, data_size_t>* paired_ranking_item_index_map_;
+  /*! \brief Number of pairwise data */
+  data_size_t num_data_;
+};
+
+}  // namespace LightGBM
+
+#endif  // LIGHTGBM_PAIRWISE_FEATURE_GROUP_H_