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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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30 changes: 26 additions & 4 deletions src/boosting/gbdt_model_text.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -632,31 +632,53 @@ std::vector<double> GBDT::FeatureImportance(int num_iteration, int importance_ty
}

std::vector<double> feature_importances(max_feature_idx_ + 1, 0.0);
bool warn_about_feature_number = false;
int max_feature_index_found = -1;
if (importance_type == 0) {
for (int iter = 0; iter < num_used_model; ++iter) {
for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) {
if (models_[iter]->split_gain(split_idx) > 0) {
const int real_feature_index = models_[iter]->split_feature(split_idx);
#ifdef DEBUG
CHECK_GE(models_[iter]->split_feature(split_idx), 0);
CHECK_GE(real_feature_index, 0);
#endif
feature_importances[models_[iter]->split_feature(split_idx)] += 1.0;
if (static_cast<size_t>(real_feature_index) >= feature_importances.size()) {
warn_about_feature_number = true;
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Provide warning instead of enlarging feature importance vector. Since GBDT::FeatureImportance is used by Python API and the python code doesn't know the final size of feature importance vector, dynamically enlarging the vector will cause out of bound access.

if (real_feature_index > max_feature_index_found) {
max_feature_index_found = real_feature_index;
}
} else {
feature_importances[real_feature_index] += 1.0;
}
}
}
}
} else if (importance_type == 1) {
for (int iter = 0; iter < num_used_model; ++iter) {
for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) {
if (models_[iter]->split_gain(split_idx) > 0) {
const int real_feature_index = models_[iter]->split_feature(split_idx);
#ifdef DEBUG
CHECK_GE(models_[iter]->split_feature(split_idx), 0);
CHECK_GE(real_feature_index, 0);
#endif
feature_importances[models_[iter]->split_feature(split_idx)] += models_[iter]->split_gain(split_idx);
if (static_cast<size_t>(real_feature_index) >= feature_importances.size()) {
warn_about_feature_number = true;
if (real_feature_index > max_feature_index_found) {
max_feature_index_found = real_feature_index;
}
} else {
feature_importances[real_feature_index] += models_[iter]->split_gain(split_idx);
}
}
}
}
} else {
Log::Fatal("Unknown importance type: only support split=0 and gain=1");
}
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);
}
return feature_importances;
}

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23 changes: 23 additions & 0 deletions tests/python_package_test/test_engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -970,6 +970,29 @@ def test_continue_train_multiclass():
assert evals_result['valid_0']['multi_logloss'][-1] == pytest.approx(ret)


def test_continue_train_different_feature_size(capsys):
np.random.seed(0)
train_X = np.hstack([np.ones(800).reshape(-1, 8), np.arange(200, 0, -1).reshape(-1, 2)])
train_y = np.sum(train_X[:, -2:], axis=1)
train_data = lgb.Dataset(train_X, label=train_y)
params = {
"objective": "regression",
"num_trees": 10,
"num_leaves": 31,
"verbose": -1,
'predict_disable_shape_check': True,
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Why is it necessary to pass predict_disable_shape_check? This test isn't generating any predictions. Can this be removed?

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@StrikerRUS StrikerRUS May 11, 2022 •
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Ah, good observation! But predict() is called internally here when you set init scores by a previously trained model

LightGBM/python-package/lightgbm/engine.py

Lines 153 to 154 in 0018206

elif isinstance(init_model, Booster):
predictor = init_model._to_predictor(dict(init_model.params, **params))

LightGBM/python-package/lightgbm/engine.py

Lines 160 to 161 in 0018206

train_set._update_params(params) \
._set_predictor(predictor) \

LightGBM/python-package/lightgbm/basic.py

Lines 1519 to 1522 in 0018206

if isinstance(predictor, _InnerPredictor):
if self._predictor is None and init_score is not None:
_log_warning("The init_score will be overridden by the prediction of init_model.")
self._set_init_score_by_predictor(predictor, data)

LightGBM/python-package/lightgbm/basic.py

Line 1393 in 0018206

def _set_init_score_by_predictor(self, predictor, data, used_indices=None):

LightGBM/python-package/lightgbm/basic.py

Lines 1399 to 1402 in 0018206

if predictor is not None:
init_score = predictor.predict(data,
raw_score=True,
data_has_header=data_has_header)

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Ah, I see! So then is the bug addressed by this PR only present when you've also set predict_disable_shape_check = True? I expect that most LightGBM users will not be aware that that prediction parameter is relevant for training continuation.

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Yes, without setting predict_disable_shape_check = True users get the following error:

LightGBMError: The number of features in data (5) is not the same as it was in training data (10).
You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.

I think the second sentence of the error "You can set predict_disable_shape_check=true to discard this error ..." helps understand this complicated relationship.

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predict_disable_shape_check is necessary because here a numpy matrix is used in the test case. If we use a LibSVM file as dataset in the test case, we don't need predict_disable_shape_check=True. I use a numpy matrix here because I think it is more convenient than LivSVM in that it does not requiring generating files in test cases.

}
model = lgb.train(train_set=train_data, params=params)

train_X_cont = np.random.rand(100, 5)
train_y_cont = np.sum(train_X_cont, axis=1)
train_data_cont = lgb.Dataset(train_X_cont, label=train_y_cont)
params.update({"verbose": 2})
lgb.train(train_set=train_data_cont, params=params, init_model=model)
captured = capsys.readouterr()
assert captured.out.find("features found in dataset for continual training, but at least") != -1


def test_cv():
X_train, y_train = make_synthetic_regression()
params = {'verbose': -1}
Expand Down