Code refactoring for ranking objective & Faster ndcg_xendcg (#2801)

* code refactoring

* update vcproject

* refine

* fix test

* Update tests/python_package_test/test_sklearn.py

* fix test

R-package/tests/testthat/test_learning_to_rank.R

@@ -20,7 +20,7 @@ test_that("learning-to-rank with lgb.train() works as expected", {
         objective = "lambdarank"
         , metric = "ndcg"
         , ndcg_at = ndcg_at
-        , max_position = 3L
+        , lambdarank_truncation_level = 3L
         , learning_rate = 0.001
     )
     model <- lgb.train(
@@ -67,7 +67,7 @@ test_that("learning-to-rank with lgb.cv() works as expected", {
         objective = "lambdarank"
         , metric = "ndcg"
         , ndcg_at = ndcg_at
-        , max_position = 3L
+        , lambdarank_truncation_level = 3L
         , label_gain = "0,1,3"
     )
     nfold <- 4L

docs/Parameters.rst

@@ -99,7 +99,9 @@ Core Parameters
 
       -  ``lambdarank``, `lambdarank <https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
 
-      -  ``rank_xendcg``, `XE_NDCG_MART <https://arxiv.org/abs/1911.09798>`__ ranking objective function. To obtain reproducible results, you should disable parallelism by setting ``num_threads`` to 1, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``
+      -  ``rank_xendcg``, `XE_NDCG_MART <https://arxiv.org/abs/1911.09798>`__ ranking objective function. aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``.
+
+      -  ``rank_xendcg`` is faster than ``lambdarank`` and achieves the similar performance as ``lambdarank``
 
       -  label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
 
@@ -801,6 +803,12 @@ Convert Parameters
 Objective Parameters
 --------------------
 
+-  ``objective_seed`` :raw-html:`<a id="objective_seed" title="Permalink to this parameter" href="#objective_seed">&#x1F517;&#xFE0E;</a>`, default = ``5``, type = int
+
+   -  random seed for objectives, if random process is needed
+
+   -  used in ``rank_xendcg``
+
 -  ``num_class`` :raw-html:`<a id="num_class" title="Permalink to this parameter" href="#num_class">&#x1F517;&#xFE0E;</a>`, default = ``1``, type = int, aliases: ``num_classes``, constraints: ``num_class > 0``
 
    -  used only in ``multi-class`` classification application
@@ -873,19 +881,19 @@ Objective Parameters
 
    -  set this closer to ``1`` to shift towards a **Poisson** distribution
 
--  ``max_position`` :raw-html:`<a id="max_position" title="Permalink to this parameter" href="#max_position">&#x1F517;&#xFE0E;</a>`, default = ``20``, type = int, constraints: ``max_position > 0``
+-  ``lambdarank_truncation_level`` :raw-html:`<a id="lambdarank_truncation_level" title="Permalink to this parameter" href="#lambdarank_truncation_level">&#x1F517;&#xFE0E;</a>`, default = ``20``, type = int, constraints: ``lambdarank_truncation_level > 0``
 
    -  used only in ``lambdarank`` application
 
-   -  optimizes `NDCG <https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG>`__ at this position
+   -  used for truncating the max_ndcg, refer to "truncation level" in the Sec.3 of `LambdaMART paper <https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf>`__ .
 
--  ``lambdamart_norm`` :raw-html:`<a id="lambdamart_norm" title="Permalink to this parameter" href="#lambdamart_norm">&#x1F517;&#xFE0E;</a>`, default = ``true``, type = bool
+-  ``lambdarank_norm`` :raw-html:`<a id="lambdarank_norm" title="Permalink to this parameter" href="#lambdarank_norm">&#x1F517;&#xFE0E;</a>`, default = ``true``, type = bool
 
    -  used only in ``lambdarank`` application
 
    -  set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data
 
-   -  set this to ``false`` to enforce the original lambdamart algorithm
+   -  set this to ``false`` to enforce the original lambdarank algorithm
 
 -  ``label_gain`` :raw-html:`<a id="label_gain" title="Permalink to this parameter" href="#label_gain">&#x1F517;&#xFE0E;</a>`, default = ``0,1,3,7,15,31,63,...,2^30-1``, type = multi-double
 
@@ -895,12 +903,6 @@ Objective Parameters
 
    -  separate by ``,``
 
--  ``objective_seed`` :raw-html:`<a id="objective_seed" title="Permalink to this parameter" href="#objective_seed">&#x1F517;&#xFE0E;</a>`, default = ``5``, type = int
-
-   -  used only in the ``rank_xendcg`` objective
-
-   -  random seed for objectives
-
 Metric Parameters
 -----------------
 

include/LightGBM/config.h

@@ -128,7 +128,8 @@ struct Config {
   // descl2 = label is anything in interval [0, 1]
   // desc = ranking application
   // descl2 = ``lambdarank``, `lambdarank <https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
-  // descl2 = ``rank_xendcg``, `XE_NDCG_MART <https://arxiv.org/abs/1911.09798>`__ ranking objective function. To obtain reproducible results, you should disable parallelism by setting ``num_threads`` to 1, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``
+  // descl2 = ``rank_xendcg``, `XE_NDCG_MART <https://arxiv.org/abs/1911.09798>`__ ranking objective function. aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``.
+  // descl2 = ``rank_xendcg`` is faster than ``lambdarank`` and achieves the similar performance as ``lambdarank``
   // descl2 = label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
   std::string objective = "regression";
 
@@ -705,6 +706,10 @@ struct Config {
 
   #pragma region Objective Parameters
 
+  // desc = random seed for objectives, if random process is needed
+  // desc = used in ``rank_xendcg``
+  int objective_seed = 5;
+
   // check = >0
   // alias = num_classes
   // desc = used only in ``multi-class`` classification application
@@ -763,13 +768,13 @@ struct Config {
 
   // check = >0
   // desc = used only in ``lambdarank`` application
-  // desc = optimizes `NDCG <https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG>`__ at this position
-  int max_position = 20;
+  // desc = used for truncating the max_ndcg, refer to "truncation level" in the Sec.3 of `LambdaMART paper <https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf>`__ .
+  int lambdarank_truncation_level = 20;
 
   // desc = used only in ``lambdarank`` application
   // desc = set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data
-  // desc = set this to ``false`` to enforce the original lambdamart algorithm
-  bool lambdamart_norm = true;
+  // desc = set this to ``false`` to enforce the original lambdarank algorithm
+  bool lambdarank_norm = true;
 
   // type = multi-double
   // default = 0,1,3,7,15,31,63,...,2^30-1
@@ -778,10 +783,6 @@ struct Config {
   // desc = separate by ``,``
   std::vector<double> label_gain;
 
-  // desc = used only in the ``rank_xendcg`` objective
-  // desc = random seed for objectives
-  int objective_seed = 5;
-
   #pragma endregion
 
   #pragma region Metric Parameters

src/io/config_auto.cpp

@@ -257,6 +257,7 @@ const std::unordered_set<std::string>& Config::parameter_set() {
   "output_result",
   "convert_model_language",
   "convert_model",
+  "objective_seed",
   "num_class",
   "is_unbalance",
   "scale_pos_weight",
@@ -267,10 +268,9 @@ const std::unordered_set<std::string>& Config::parameter_set() {
   "fair_c",
   "poisson_max_delta_step",
   "tweedie_variance_power",
-  "max_position",
-  "lambdamart_norm",
+  "lambdarank_truncation_level",
+  "lambdarank_norm",
   "label_gain",
-  "objective_seed",
   "metric",
   "metric_freq",
   "is_provide_training_metric",
@@ -513,6 +513,8 @@ void Config::GetMembersFromString(const std::unordered_map<std::string, std::str
 
   GetString(params, "convert_model", &convert_model);
 
+  GetInt(params, "objective_seed", &objective_seed);
+
   GetInt(params, "num_class", &num_class);
   CHECK(num_class >0);
 
@@ -541,17 +543,15 @@ void Config::GetMembersFromString(const std::unordered_map<std::string, std::str
   CHECK(tweedie_variance_power >=1.0);
   CHECK(tweedie_variance_power <2.0);
 
-  GetInt(params, "max_position", &max_position);
-  CHECK(max_position >0);
+  GetInt(params, "lambdarank_truncation_level", &lambdarank_truncation_level);
+  CHECK(lambdarank_truncation_level >0);
 
-  GetBool(params, "lambdamart_norm", &lambdamart_norm);
+  GetBool(params, "lambdarank_norm", &lambdarank_norm);
 
   if (GetString(params, "label_gain", &tmp_str)) {
     label_gain = Common::StringToArray<double>(tmp_str, ',');
   }
 
-  GetInt(params, "objective_seed", &objective_seed);
-
   GetInt(params, "metric_freq", &metric_freq);
   CHECK(metric_freq >0);
 
@@ -675,6 +675,7 @@ std::string Config::SaveMembersToString() const {
   str_buf << "[output_result: " << output_result << "]\n";
   str_buf << "[convert_model_language: " << convert_model_language << "]\n";
   str_buf << "[convert_model: " << convert_model << "]\n";
+  str_buf << "[objective_seed: " << objective_seed << "]\n";
   str_buf << "[num_class: " << num_class << "]\n";
   str_buf << "[is_unbalance: " << is_unbalance << "]\n";
   str_buf << "[scale_pos_weight: " << scale_pos_weight << "]\n";
@@ -685,10 +686,9 @@ std::string Config::SaveMembersToString() const {
   str_buf << "[fair_c: " << fair_c << "]\n";
   str_buf << "[poisson_max_delta_step: " << poisson_max_delta_step << "]\n";
   str_buf << "[tweedie_variance_power: " << tweedie_variance_power << "]\n";
-  str_buf << "[max_position: " << max_position << "]\n";
-  str_buf << "[lambdamart_norm: " << lambdamart_norm << "]\n";
+  str_buf << "[lambdarank_truncation_level: " << lambdarank_truncation_level << "]\n";
+  str_buf << "[lambdarank_norm: " << lambdarank_norm << "]\n";
   str_buf << "[label_gain: " << Common::Join(label_gain, ",") << "]\n";
-  str_buf << "[objective_seed: " << objective_seed << "]\n";
   str_buf << "[metric_freq: " << metric_freq << "]\n";
   str_buf << "[is_provide_training_metric: " << is_provide_training_metric << "]\n";
   str_buf << "[eval_at: " << Common::Join(eval_at, ",") << "]\n";

src/objective/objective_function.cpp

@@ -7,7 +7,6 @@
 #include "binary_objective.hpp"
 #include "multiclass_objective.hpp"
 #include "rank_objective.hpp"
-#include "rank_xendcg_objective.hpp"
 #include "regression_objective.hpp"
 #include "xentropy_objective.hpp"
 

src/objective/rank_objective.hpp

 /*!
- * Copyright (c) 2016 Microsoft Corporation. All rights reserved.
- * Licensed under the MIT License. See LICENSE file in the project root for license information.
+ * Copyright (c) 2020 Microsoft Corporation. All rights reserved.
+ * Licensed under the MIT License. See LICENSE file in the project root for
+ * license information.
  */
 #ifndef LIGHTGBM_OBJECTIVE_RANK_OBJECTIVE_HPP_
 #define LIGHTGBM_OBJECTIVE_RANK_OBJECTIVE_HPP_
@@ -8,29 +9,102 @@
 #include <LightGBM/metric.h>
 #include <LightGBM/objective_function.h>
 
-#include <limits>
-#include <string>
 #include <algorithm>
 #include <cmath>
 #include <cstdio>
 #include <cstring>
+#include <limits>
+#include <string>
 #include <vector>
 
 namespace LightGBM {
+
+/*!
+ * \brief Objective function for Ranking
+ */
+class RankingObjective : public ObjectiveFunction {
+ public:
+  explicit RankingObjective(const Config& config)
+      : seed_(config.objective_seed) {}
+
+  explicit RankingObjective(const std::vector<std::string>&) : seed_(0) {}
+
+  ~RankingObjective() {}
+
+  void Init(const Metadata& metadata, data_size_t num_data) override {
+    num_data_ = num_data;
+    // get label
+    label_ = metadata.label();
+    // get weights
+    weights_ = metadata.weights();
+    // get boundries
+    query_boundaries_ = metadata.query_boundaries();
+    if (query_boundaries_ == nullptr) {
+      Log::Fatal("Ranking tasks require query information");
+    }
+    num_queries_ = metadata.num_queries();
+  }
+
+  void GetGradients(const double* score, score_t* gradients,
+                    score_t* hessians) const override {
+#pragma omp parallel for schedule(guided)
+    for (data_size_t i = 0; i < num_queries_; ++i) {
+      const data_size_t start = query_boundaries_[i];
+      const data_size_t cnt = query_boundaries_[i + 1] - query_boundaries_[i];
+      GetGradientsForOneQuery(i, cnt, label_ + start, score + start,
+                              gradients + start, hessians + start);
+      if (weights_ != nullptr) {
+        for (data_size_t j = 0; j < cnt; ++j) {
+          gradients[start + j] =
+              static_cast<score_t>(gradients[start + j] * weights_[start + j]);
+          hessians[start + j] =
+              static_cast<score_t>(hessians[start + j] * weights_[start + j]);
+        }
+      }
+    }
+  }
+
+  virtual void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt,
+                                       const label_t* label,
+                                       const double* score, score_t* lambdas,
+                                       score_t* hessians) const = 0;
+
+  virtual const char* GetName() const override = 0;
+
+  std::string ToString() const override {
+    std::stringstream str_buf;
+    str_buf << GetName();
+    return str_buf.str();
+  }
+
+  bool NeedAccuratePrediction() const override { return false; }
+
+ protected:
+  int seed_;
+  data_size_t num_queries_;
+  /*! \brief Number of data */
+  data_size_t num_data_;
+  /*! \brief Pointer of label */
+  const label_t* label_;
+  /*! \brief Pointer of weights */
+  const label_t* weights_;
+  /*! \brief Query boundries */
+  const data_size_t* query_boundaries_;
+};
 /*!
-* \brief Objective function for Lambdrank with NDCG
-*/
-class LambdarankNDCG: public ObjectiveFunction {
+ * \brief Objective function for Lambdrank with NDCG
+ */
+class LambdarankNDCG : public RankingObjective {
  public:
-  explicit LambdarankNDCG(const Config& config) {
-    sigmoid_ = static_cast<double>(config.sigmoid);
-    norm_ = config.lambdamart_norm;
+  explicit LambdarankNDCG(const Config& config)
+      : RankingObjective(config),
+        sigmoid_(config.sigmoid),
+        norm_(config.lambdarank_norm),
+        truncation_level_(config.lambdarank_truncation_level) {
     label_gain_ = config.label_gain;
     // initialize DCG calculator
     DCGCalculator::DefaultLabelGain(&label_gain_);
     DCGCalculator::Init(label_gain_);
-    // will optimize NDCG@optimize_pos_at_
-    optimize_pos_at_ = config.max_position;
     sigmoid_table_.clear();
     inverse_max_dcgs_.clear();
     if (sigmoid_ <= 0.0) {
@@ -38,30 +112,19 @@ class LambdarankNDCG: public ObjectiveFunction {
     }
   }
 
-  explicit LambdarankNDCG(const std::vector<std::string>&) {
-  }
+  explicit LambdarankNDCG(const std::vector<std::string>& strs)
+      : RankingObjective(strs) {}
+
+  ~LambdarankNDCG() {}
 
-  ~LambdarankNDCG() {
-  }
   void Init(const Metadata& metadata, data_size_t num_data) override {
-    num_data_ = num_data;
-    // get label
-    label_ = metadata.label();
+    RankingObjective::Init(metadata, num_data);
     DCGCalculator::CheckLabel(label_, num_data_);
-    // get weights
-    weights_ = metadata.weights();
-    // get boundries
-    query_boundaries_ = metadata.query_boundaries();
-    if (query_boundaries_ == nullptr) {
-      Log::Fatal("Lambdarank tasks require query information");
-    }
-    num_queries_ = metadata.num_queries();
-    // cache inverse max DCG, avoid computation many times
     inverse_max_dcgs_.resize(num_queries_);
 #pragma omp parallel for schedule(static)
     for (data_size_t i = 0; i < num_queries_; ++i) {
-      inverse_max_dcgs_[i] = DCGCalculator::CalMaxDCGAtK(optimize_pos_at_,
-        label_ + query_boundaries_[i],
+      inverse_max_dcgs_[i] = DCGCalculator::CalMaxDCGAtK(
+          truncation_level_, label_ + query_boundaries_[i],
           query_boundaries_[i + 1] - query_boundaries_[i]);
 
       if (inverse_max_dcgs_[i] > 0.0) {
@@ -72,38 +135,24 @@ class LambdarankNDCG: public ObjectiveFunction {
     ConstructSigmoidTable();
   }
 
-  void GetGradients(const double* score, score_t* gradients,
+  inline void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt,
+                                      const label_t* label, const double* score,
+                                      score_t* lambdas,
                                       score_t* hessians) const override {
-    #pragma omp parallel for schedule(guided)
-    for (data_size_t i = 0; i < num_queries_; ++i) {
-      GetGradientsForOneQuery(score, gradients, hessians, i);
-    }
-  }
-
-  inline void GetGradientsForOneQuery(const double* score,
-              score_t* lambdas, score_t* hessians, data_size_t query_id) const {
-    // get doc boundary for current query
-    const data_size_t start = query_boundaries_[query_id];
-    const data_size_t cnt =
-      query_boundaries_[query_id + 1] - query_boundaries_[query_id];
     // get max DCG on current query
     const double inverse_max_dcg = inverse_max_dcgs_[query_id];
-    // add pointers with offset
-    const label_t* label = label_ + start;
-    score += start;
-    lambdas += start;
-    hessians += start;
     // initialize with zero
     for (data_size_t i = 0; i < cnt; ++i) {
       lambdas[i] = 0.0f;
       hessians[i] = 0.0f;
     }
     // get sorted indices for scores
-    std::vector<data_size_t> sorted_idx;
+    std::vector<data_size_t> sorted_idx(cnt);
     for (data_size_t i = 0; i < cnt; ++i) {
-      sorted_idx.emplace_back(i);
+      sorted_idx[i] = i;
     }
-    std::stable_sort(sorted_idx.begin(), sorted_idx.end(),
+    std::stable_sort(
+        sorted_idx.begin(), sorted_idx.end(),
         [score](data_size_t a, data_size_t b) { return score[a] > score[b]; });
     // get best and worst score
     const double best_score = score[sorted_idx[0]];
@@ -118,20 +167,25 @@ class LambdarankNDCG: public ObjectiveFunction {
       const data_size_t high = sorted_idx[i];
       const int high_label = static_cast<int>(label[high]);
       const double high_score = score[high];
-      if (high_score == kMinScore) { continue; }
+      if (high_score == kMinScore) {
+        continue;
+      }
       const double high_label_gain = label_gain_[high_label];
       const double high_discount = DCGCalculator::GetDiscount(i);
       double high_sum_lambda = 0.0;
       double high_sum_hessian = 0.0;
       for (data_size_t j = 0; j < cnt; ++j) {
         // skip same data
-        if (i == j) { continue; }
-
+        if (i == j) {
+          continue;
+        }
         const data_size_t low = sorted_idx[j];
         const int low_label = static_cast<int>(label[low]);
         const double low_score = score[low];
         // only consider pair with different label
-        if (high_label <= low_label || low_score == kMinScore) { continue; }
+        if (high_label <= low_label || low_score == kMinScore) {
+          continue;
+        }
 
         const double delta_score = high_score - low_score;
 
@@ -144,7 +198,7 @@ class LambdarankNDCG: public ObjectiveFunction {
         // get delta NDCG
         double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg;
         // regular the delta_pair_NDCG by score distance
-        if (norm_ && high_label != low_label && best_score != worst_score) {
+        if (norm_ && best_score != worst_score) {
           delta_pair_NDCG /= (0.01f + fabs(delta_score));
         }
         // calculate lambda for this pair
@@ -171,25 +225,18 @@ class LambdarankNDCG: public ObjectiveFunction {
         hessians[i] = static_cast<score_t>(hessians[i] * norm_factor);
       }
     }
-    // if need weights
-    if (weights_ != nullptr) {
-      for (data_size_t i = 0; i < cnt; ++i) {
-        lambdas[i] = static_cast<score_t>(lambdas[i] * weights_[start + i]);
-        hessians[i] = static_cast<score_t>(hessians[i] * weights_[start + i]);
-      }
-    }
   }
 
-
   inline double GetSigmoid(double score) const {
     if (score <= min_sigmoid_input_) {
       // too small, use lower bound
       return sigmoid_table_[0];
     } else if (score >= max_sigmoid_input_) {
-      // too big, use upper bound
+      // too large, use upper bound
       return sigmoid_table_[_sigmoid_bins - 1];
     } else {
-      return sigmoid_table_[static_cast<size_t>((score - min_sigmoid_input_) * sigmoid_table_idx_factor_)];
+      return sigmoid_table_[static_cast<size_t>((score - min_sigmoid_input_) *
+                                                sigmoid_table_idx_factor_)];
     }
   }
 
@@ -208,41 +255,20 @@ class LambdarankNDCG: public ObjectiveFunction {
     }
   }
 
-  const char* GetName() const override {
-    return "lambdarank";
-  }
-
-  std::string ToString() const override {
-    std::stringstream str_buf;
-    str_buf << GetName();
-    return str_buf.str();
-  }
-
-  bool NeedAccuratePrediction() const override { return false; }
+  const char* GetName() const override { return "lambdarank"; }
 
  private:
-  /*! \brief Gains for labels */
-  std::vector<double> label_gain_;
-  /*! \brief Cache inverse max DCG, speed up calculation */
-  std::vector<double> inverse_max_dcgs_;
   /*! \brief Simgoid param */
   double sigmoid_;
   /*! \brief Normalize the lambdas or not */
   bool norm_;
-  /*! \brief Optimized NDCG@ */
-  int optimize_pos_at_;
-  /*! \brief Number of queries */
-  data_size_t num_queries_;
-  /*! \brief Number of data */
-  data_size_t num_data_;
-  /*! \brief Pointer of label */
-  const label_t* label_;
-  /*! \brief Pointer of weights */
-  const label_t* weights_;
-  /*! \brief Query boundries */
-  const data_size_t* query_boundaries_;
+  /*! \brief truncation position for max ndcg */
+  int truncation_level_;
+  /*! \brief Cache inverse max DCG, speed up calculation */
+  std::vector<double> inverse_max_dcgs_;
   /*! \brief Cache result for sigmoid transform to speed up */
   std::vector<double> sigmoid_table_;
+  std::vector<double> label_gain_;
   /*! \brief Number of bins in simoid table */
   size_t _sigmoid_bins = 1024 * 1024;
   /*! \brief Minimal input of sigmoid table */
@@ -253,5 +279,82 @@ class LambdarankNDCG: public ObjectiveFunction {
   double sigmoid_table_idx_factor_;
 };
 
+/*!
+ * \brief Implementation of the learning-to-rank objective function, XE_NDCG
+ * [arxiv.org/abs/1911.09798].
+ */
+class RankXENDCG : public RankingObjective {
+ public:
+  explicit RankXENDCG(const Config& config) : RankingObjective(config) {}
+
+  explicit RankXENDCG(const std::vector<std::string>& strs)
+      : RankingObjective(strs) {}
+
+  ~RankXENDCG() {}
+
+  void Init(const Metadata& metadata, data_size_t num_data) override {
+    RankingObjective::Init(metadata, num_data);
+    for (data_size_t i = 0; i < num_queries_; ++i) {
+      rands_.emplace_back(seed_ + i);
+    }
+  }
+
+  inline void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt,
+                                      const label_t* label, const double* score,
+                                      score_t* lambdas,
+                                      score_t* hessians) const override {
+    // Turn scores into a probability distribution using Softmax.
+    std::vector<double> rho(cnt, 0.0);
+    Common::Softmax(score, rho.data(), cnt);
+
+    // used for Phi and L1
+    std::vector<double> l1s(cnt);
+    double sum_labels = 0;
+    for (data_size_t i = 0; i < cnt; ++i) {
+      l1s[i] = Phi(label[i], rands_[query_id].NextFloat());
+      sum_labels += l1s[i];
+    }
+    // sum_labels will always be positive number
+    sum_labels = std::max<double>(kEpsilon, sum_labels);
+    // Approximate gradients and inverse Hessian.
+    // First order terms.
+    double sum_l1 = 0.0f;
+    for (data_size_t i = 0; i < cnt; ++i) {
+      l1s[i] = -l1s[i] / sum_labels + rho[i];
+      sum_l1 += l1s[i];
+    }
+    if (cnt <= 1) {
+      // when cnt <= 1, the l2 and l3 are zeros
+      for (data_size_t i = 0; i < cnt; ++i) {
+        lambdas[i] = static_cast<score_t>(l1s[i]);
+        hessians[i] = static_cast<score_t>(rho[i] * (1.0 - rho[i]));
+      }
+    } else {
+      // Second order terms.
+      std::vector<double> l2s(cnt, 0.0);
+      double sum_l2 = 0.0;
+      for (data_size_t i = 0; i < cnt; ++i) {
+        l2s[i] = (sum_l1 - l1s[i]) / (1 - rho[i]);
+        sum_l2 += l2s[i];
+      }
+      for (data_size_t i = 0; i < cnt; ++i) {
+        auto l3 = (sum_l2 - l2s[i]) / (1 - rho[i]);
+        lambdas[i] = static_cast<score_t>(l1s[i] + rho[i] * l2s[i] +
+                                          rho[i] * rho[i] * l3);
+        hessians[i] = static_cast<score_t>(rho[i] * (1.0 - rho[i]));
+      }
+    }
+  }
+
+  double Phi(const label_t l, double g) const {
+    return Common::Pow(2, static_cast<int>(l)) - g;
+  }
+
+  const char* GetName() const override { return "rank_xendcg"; }
+
+ private:
+  mutable std::vector<Random> rands_;
+};
+
 }  // namespace LightGBM
 #endif  // LightGBM_OBJECTIVE_RANK_OBJECTIVE_HPP_

src/objective/rank_xendcg_objective.hpp

-/*!
- * Copyright (c) 2019 Microsoft Corporation. All rights reserved.
- * Licensed under the MIT License. See LICENSE file in the project root for license information.
- */
-#ifndef LIGHTGBM_OBJECTIVE_RANK_XENDCG_OBJECTIVE_HPP_
-#define LIGHTGBM_OBJECTIVE_RANK_XENDCG_OBJECTIVE_HPP_
-
-#include <LightGBM/objective_function.h>
-#include <LightGBM/utils/common.h>
-#include <LightGBM/utils/random.h>
-
-#include <string>
-#include <vector>
-
-namespace LightGBM {
-/*!
-* \brief Implementation of the learning-to-rank objective function, XE_NDCG [arxiv.org/abs/1911.09798].
-*/
-class RankXENDCG: public ObjectiveFunction {
- public:
-  explicit RankXENDCG(const Config& config) {
-    rand_ = new Random(config.objective_seed);
-  }
-
-  explicit RankXENDCG(const std::vector<std::string>&) {
-    rand_ = new Random();
-  }
-
-  ~RankXENDCG() {
-  }
-  void Init(const Metadata& metadata, data_size_t) override {
-    // get label
-    label_ = metadata.label();
-    // get boundries
-    query_boundaries_ = metadata.query_boundaries();
-    if (query_boundaries_ == nullptr) {
-      Log::Fatal("RankXENDCG tasks require query information");
-    }
-    num_queries_ = metadata.num_queries();
-  }
-
-  void GetGradients(const double* score, score_t* gradients,
-                    score_t* hessians) const override {
-    #pragma omp parallel for schedule(guided)
-    for (data_size_t i = 0; i < num_queries_; ++i) {
-      GetGradientsForOneQuery(score, gradients, hessians, i);
-    }
-  }
-
-  inline void GetGradientsForOneQuery(
-      const double* score,
-      score_t* lambdas, score_t* hessians, data_size_t query_id) const {
-    // get doc boundary for current query
-    const data_size_t start = query_boundaries_[query_id];
-    const data_size_t cnt =
-      query_boundaries_[query_id + 1] - query_boundaries_[query_id];
-    // add pointers with offset
-    const label_t* label = label_ + start;
-    score += start;
-    lambdas += start;
-    hessians += start;
-
-    // Turn scores into a probability distribution using Softmax.
-    std::vector<double> rho(cnt);
-    Common::Softmax(score, &rho[0], cnt);
-
-    // Prepare a vector of gammas, a parameter of the loss.
-    std::vector<double> gammas(cnt);
-    for (data_size_t i = 0; i < cnt; ++i) {
-      gammas[i] = rand_->NextFloat();
-    }
-
-    // Skip query if sum of labels is 0.
-    float sum_labels = 0;
-    for (data_size_t i = 0; i < cnt; ++i) {
-      sum_labels += static_cast<float>(phi(label[i], gammas[i]));
-    }
-    if (std::fabs(sum_labels) < kEpsilon) {
-      return;
-    }
-
-    // Approximate gradients and inverse Hessian.
-    // First order terms.
-    std::vector<double> L1s(cnt);
-    for (data_size_t i = 0; i < cnt; ++i) {
-      L1s[i] = -phi(label[i], gammas[i])/sum_labels + rho[i];
-    }
-    // Second-order terms.
-    std::vector<double> L2s(cnt);
-    for (data_size_t i = 0; i < cnt; ++i) {
-      for (data_size_t j = 0; j < cnt; ++j) {
-        if (i == j) continue;
-        L2s[i] += L1s[j] / (1 - rho[j]);
-      }
-    }
-    // Third-order terms.
-    std::vector<double> L3s(cnt);
-    for (data_size_t i = 0; i < cnt; ++i) {
-      for (data_size_t j = 0; j < cnt; ++j) {
-        if (i == j) continue;
-        L3s[i] += rho[j] * L2s[j] / (1 - rho[j]);
-      }
-    }
-
-    // Finally, prepare lambdas and hessians.
-    for (data_size_t i = 0; i < cnt; ++i) {
-      lambdas[i] = static_cast<score_t>(
-          L1s[i] + rho[i]*L2s[i] + rho[i]*L3s[i]);
-      hessians[i] = static_cast<score_t>(rho[i] * (1.0 - rho[i]));
-    }
-  }
-
-  double phi(const label_t l, double g) const {
-    return Common::Pow(2, static_cast<int>(l)) - g;
-  }
-
-  const char* GetName() const override {
-    return "rank_xendcg";
-  }
-
-  std::string ToString() const override {
-    std::stringstream str_buf;
-    str_buf << GetName();
-    return str_buf.str();
-  }
-
-  bool NeedAccuratePrediction() const override { return false; }
-
- private:
-  /*! \brief Number of queries */
-  data_size_t num_queries_;
-  /*! \brief Pointer of label */
-  const label_t* label_;
-  /*! \brief Query boundries */
-  const data_size_t* query_boundaries_;
-  /*! \brief Pseudo-random number generator */
-  Random* rand_;
-};
-
-}  // namespace LightGBM
-#endif   // LightGBM_OBJECTIVE_RANK_XENDCG_OBJECTIVE_HPP_

tests/python_package_test/test_sklearn.py

@@ -129,8 +129,8 @@ class TestSklearn(unittest.TestCase):
                 eval_metric='ndcg',
                 callbacks=[lgb.reset_parameter(learning_rate=lambda x: max(0.01, 0.1 - 0.01 * x))])
         self.assertLessEqual(gbm.best_iteration_, 24)
-        self.assertGreater(gbm.best_score_['valid_0']['ndcg@1'], 0.6559)
-        self.assertGreater(gbm.best_score_['valid_0']['ndcg@3'], 0.6421)
+        self.assertGreater(gbm.best_score_['valid_0']['ndcg@1'], 0.6382)
+        self.assertGreater(gbm.best_score_['valid_0']['ndcg@3'], 0.6319)
 
     def test_regression_with_custom_objective(self):
         X, y = load_boston(True)

windows/LightGBM.vcxproj

@@ -243,7 +243,6 @@
     <ClInclude Include="..\src\network\socket_wrapper.hpp" />
     <ClInclude Include="..\src\objective\binary_objective.hpp" />
     <ClInclude Include="..\src\objective\rank_objective.hpp" />
-    <ClInclude Include="..\src\objective\rank_xendcg_objective.hpp" />
     <ClInclude Include="..\src\objective\regression_objective.hpp" />
     <ClInclude Include="..\src\objective\multiclass_objective.hpp" />
     <ClInclude Include="..\src\objective\xentropy_objective.hpp" />

windows/LightGBM.vcxproj.filters

@@ -84,9 +84,6 @@
     <ClInclude Include="..\src\objective\rank_objective.hpp">
       <Filter>src\objective</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\objective\rank_xendcg_objective.hpp">
-      <Filter>src\objective</Filter>
-    </ClInclude>
     <ClInclude Include="..\src\objective\regression_objective.hpp">
       <Filter>src\objective</Filter>
     </ClInclude>