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"