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Fix number of features mismatching in continual training (fix #5156) #5157

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Fix number of features mismatching in continual training (fix #5156) #5157

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24b7df0
consider number of features from model in continual training
shiyu1994 Apr 17, 2022
442c03a
rollback changes
shiyu1994 Apr 21, 2022
2887ac0
handle mismatches only within gbdt_model_text.cpp
shiyu1994 Apr 21, 2022
0a20a13
remove useless debug information
shiyu1994 May 5, 2022
bda5bdb
add a test case
shiyu1994 May 9, 2022
7e7190f
Update tests/python_package_test/test_engine.py
shiyu1994 May 10, 2022
adb8982
Merge branch 'master' of https://github.com/Microsoft/LightGBM into f…
shiyu1994 May 10, 2022
c823137
provide warning instead of enlarging feature importances
shiyu1994 May 10, 2022
750d35c
Merge branch 'fix-5156' of https://github.com/shiyu1994/LightGBM into…
shiyu1994 May 10, 2022
3e52538
remove useless comments
shiyu1994 May 10, 2022
23e9991
ensure the warning is raised in test case
shiyu1994 May 11, 2022
d719d78
add suggestion for the warning
shiyu1994 May 30, 2022
aab8d1f
Merge branch 'master' into fix-5156
shiyu1994 Mar 1, 2023
1f09936
Merge remote-tracking branch 'origin/master' into fix-5156
shiyu1994 Mar 15, 2023
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add suggestion for the warning
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@shiyu1994
shiyu1994 committed May 30, 2022
commit d719d78d867532267e4c7f28fa7bb6ba6f273eb0
1 change: 1 addition & 0 deletions src/boosting/gbdt_model_text.cpp
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Expand Up @@ -678,6 +678,7 @@ std::vector<double> GBDT::FeatureImportance(int num_iteration, int importance_ty
if (warn_about_feature_number) {
Log::Warning("Only %d features found in dataset for continual training, but at least %d features found in initial model.",
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Is it possible to add some information here that can help users understand what they should do about this warning? Or is this warning just being raised for the purpose of the unit test?

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I've added

Log::Warning("Please check the number of features used in continual training.");

in d719d78.

Do you think it is appropriate?

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Thanks! But I don't think "please check" will be any more informative for users, and I think that providing two separate WARN-level log messages for the same problem might be confusing.

Would you consider this?

Log::Warning(
    "Only %d features found in dataset, but at least %d features found in initial model. "
    "If this is intentional and the features in dataset match those used to train the initial model, no action is required."
    "If not, training continuation may fail or produce bad results."
    "To suppress this warning, provide a dataset with at least %d features"
    static_cast<int>(feature_importances.size()),
    max_feature_index_found + 1,
    max_feature_index_found + 1
)

static_cast<int>(feature_importances.size()), max_feature_index_found + 1);
Log::Warning("Please check the number of features used in continual training.");
}
return feature_importances;
}
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