Add support to optimize for NDCG at a given truncation level #3425

metpavel · 2020-09-30T23:10:37Z

In order to correctly optimize for NDCG@_k_, one should exclude pairs containing both documents beyond the top-_k_ (as they don't affect NDCG@_k_ when swapped).

ghost · 2020-09-30T23:10:52Z

All CLA requirements met.

guolinke · 2020-09-30T23:19:32Z

Any benchmarks for the change?

metpavel · 2020-10-01T22:49:06Z

Any benchmarks for the change?

For MSLR-WEB30K dataset (fold 1) using default train.conf, performance on the validation set:
Optimize for NDCG@FullRank:
ndcg@1 : 0.498897
ndcg@3 : 0.484631
ndcg@5 : 0.487863
208.163323 seconds elapsed, finished iteration 100

Optimize for NDCG@20:
ndcg@1 : 0.502574
ndcg@3 : 0.486615
ndcg@5 : 0.489424
124.763394 seconds elapsed, finished iteration 100

Optimize for NDCG@10:
ndcg@1 : 0.504389
ndcg@3 : 0.487059
ndcg@5 : 0.48924
93.361508 seconds elapsed, finished iteration 100

Optimize for NDCG@5:
ndcg@1 : 0.498769
ndcg@3 : 0.483403
ndcg@5 : 0.48611
96.900527 seconds elapsed, finished iteration 100

Optimize for NDCG@3:
ndcg@1 : 0.503184
ndcg@3 : 0.483097
ndcg@5 : 0.485419
79.423862 seconds elapsed, finished iteration 100

Optimize for NDCG@1:
ndcg@1 : 0.502715
ndcg@3 : 0.47437
ndcg@5 : 0.47624
71.433821 seconds elapsed, finished iteration 100

guolinke · 2020-10-09T07:28:31Z

I think this PR is a new feature.

guolinke · 2020-10-09T07:29:52Z

@metpavel Is the result of "full rank" the same as before?

guolinke · 2020-10-09T07:33:05Z

src/objective/rank_objective.hpp

-        const data_size_t low = sorted_idx[j];
+    // start accmulate lambdas by pairs that contain at least one document above truncation level
+    for (data_size_t i = 0; i < cnt - 1 && i < truncation_level_; ++i) {
+      for (data_size_t j = i + 1; j < cnt; ++j) {


Isn't this start from '0' ? if not, why?

updated:
I see. never mind.

src/objective/rank_objective.hpp

guolinke · 2020-10-09T07:51:21Z

src/objective/rank_objective.hpp

+        const double high_label_gain = label_gain_[high_label];
+        const double high_discount = DCGCalculator::GetDiscount(high_rank);
+
+        const data_size_t low_rank = label[sorted_idx[i]] > label[sorted_idx[j]] ? j : i;


remove the additional branching? get high_rank and low_rank by one if.

Co-authored-by: Guolin Ke <[email protected]>

remove the additional branching: get high_rank and low_rank by one "if".

metpavel · 2020-10-09T11:40:13Z

@metpavel Is the result of "full rank" the same as before?

@guolinke, yes

guolinke · 2020-10-09T12:23:24Z

From the results, it seems we need a slightly larger truncation level, e.g. k + m, to optimize NDCG@k?

metpavel · 2020-10-09T16:30:50Z

From the results, it seems we need a slightly larger truncation level, e.g. k + m, to optimize NDCG@k?

@guolinke, perhaps one may want to treat that m as a tunable hyper-parameter to balance between better alignment with the desired cutoff k (smaller m) and more pairs to train on (larger m)

guolinke · 2020-10-09T23:15:45Z

@metpavel Yes, maybe you can add this description into the document of truncation level?

metpavel · 2020-10-10T21:18:47Z

@metpavel Yes, maybe you can add this description into the document of truncation level?

@guolinke, do you mean here: /blob/master/docs/Parameters.rst?

guolinke · 2020-10-11T01:13:04Z

@metpavel we usually write the doc for parameters in config.h, and then run helpers/parameter_generator.py to auto-update .rst file.

guolinke · 2020-10-18T07:02:36Z

BTW, can you also merge master, to ensure CI passes?

add description to lambdarank_truncation_level parameter

guolinke · 2020-10-22T13:27:36Z

@jameslamb can you help to fix the R's tests? It needs to update some ranking score constraints, as the ranking objective is updated.

update expected NDCG value for a test, as it was affected by the underlying change in the algorithm

update NDCG@3 reference value

jameslamb · 2020-10-23T04:46:35Z

@jameslamb can you help to fix the R's tests? It needs to update some ranking score constraints, as the ranking objective is updated.

ok sure, I can help

jameslamb · 2020-10-23T05:09:50Z

@metpavel I just created a pull request into this branch on your fork. If you merge it, it should fix the R tests

metpavel#1

jameslamb · 2020-10-23T05:13:01Z

src/objective/rank_objective.hpp

        // lambda is negative, so use minus to accumulate
        sum_lambdas -= 2 * p_lambda;
      }
-      // update
-      lambdas[high] += static_cast<score_t>(high_sum_lambda);
-      hessians[high] += static_cast<score_t>(high_sum_hessian);
    }
    if (norm_ && sum_lambdas > 0) {
      double norm_factor = std::log2(1 + sum_lambdas) / sum_lambdas;


/gha run-valgrind

/gha run r-valgrind

^ this comment started this run checking that these changes pass our valgrind tests: https://github.com/microsoft/LightGBM/runs/1296338580?check_suite_focus=true

this didn't show any new issues...but I'll run it again once my fixes are merged into this PR: #3425 (comment)

/gha run-valgrind

/gha run r-valgrind

if this passes, I'll come back and approve: https://github.com/microsoft/LightGBM/runs/1310275649?check_suite_focus=true

fix R learning-to-rank tests

include/LightGBM/config.h

Co-authored-by: Guolin Ke <[email protected]>

guolinke · 2020-10-26T13:13:46Z

You need to re-run the parameter generator, due to the default value is changed.

github-actions · 2023-08-24T04:39:48Z

This pull request has been automatically locked since there has not been any recent activity since it was closed. To start a new related discussion, open a new issue at https://github.com/microsoft/LightGBM/issues including a reference to this.

Add support to optimize for NDCG at a given truncation level

16955e0

In order to correctly optimize for NDCG@_k_, one should exclude pairs containing both documents beyond the top-_k_ (as they don't affect NDCG@_k_ when swapped).

metpavel requested review from btrotta, chivee and guolinke as code owners September 30, 2020 23:10

Update rank_objective.hpp

d938551

jameslamb added the fix label Oct 6, 2020

guolinke added feature and removed fix labels Oct 9, 2020

guolinke reviewed Oct 9, 2020

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metpavel and others added 2 commits October 9, 2020 14:25

Apply suggestions from code review

b2736e6

Co-authored-by: Guolin Ke <[email protected]>

Update rank_objective.hpp

9e3cc2b

remove the additional branching: get high_rank and low_rank by one "if".

metpavel added 2 commits October 20, 2020 22:43

Update config.h

c7890dc

add description to lambdarank_truncation_level parameter

Update Parameters.rst

7f36817

metpavel requested review from jameslamb, Laurae2 and StrikerRUS as code owners October 21, 2020 23:41

Merge branch 'master' into patch-1

4d7183b

Update test_sklearn.py

c15423f

update expected NDCG value for a test, as it was affected by the underlying change in the algorithm

metpavel requested review from henry0312 and wxchan as code owners October 22, 2020 13:39

Update test_sklearn.py

8e98bd4

update NDCG@3 reference value

fix R learning-to-rank tests

a06485a

jameslamb mentioned this pull request Oct 23, 2020

fix R learning-to-rank tests metpavel/LightGBM#1

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metpavel added 3 commits October 26, 2020 00:27

Merge pull request #1 from jameslamb/fix/r-ltr-tests

c51fb3a

fix R learning-to-rank tests

Merge branch 'master' into patch-1

7bfc652

Update rank_objective.hpp

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guolinke mentioned this pull request Oct 26, 2020

3.1.0 release #3484

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Update include/LightGBM/config.h

b6bd92f

Co-authored-by: Guolin Ke <[email protected]>

Update Parameters.rst

e73d3db

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guolinke merged commit ba0a1f8 into microsoft:master Oct 27, 2020

metpavel deleted the patch-1 branch October 27, 2020 18:29

jameslamb mentioned this pull request Nov 1, 2020

[R-package] learning-to-rank tests are broken on Solaris 10 and 32-bit Windows #3513

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guolinke mentioned this pull request Nov 13, 2020

[feature request] faster lambdarank #2701

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StrikerRUS mentioned this pull request Jun 9, 2021

Consistent release version-correlated decrease of LGBMRanker performance #4349

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Add support to optimize for NDCG at a given truncation level #3425

Add support to optimize for NDCG at a given truncation level #3425

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Add support to optimize for NDCG at a given truncation level #3425

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