Fix evalution of linear trees with a single leaf.

[mjmckp]

Note that trees without linear models at the leaf always handle num_leaves = 1 as a special case and directly output the leaf value. Linear trees were missing this special case handling, and hence would have the following issues:

• Calling Tree::Predict or Tree::PredictByMap would cause an access violation exception attempting to access the first value of the empty split_feature_ array in GetLeaf.
• PredictionFunLinear would either cause an access violation or go into an infinite loop when attempting to do the equivalent of GetLeaf.

Note also that PredictionFun does not need the same changes as PredictionFunLinear, since both are only called by Tree::AddPredictionToScore, which has a special case for (!is_linear_ && num_leaves_ <= 1) that precludes calling PredictionFun.

[guolinke]

cc @btrotta

[btrotta]

@mjmckp Thanks for finding and fixing this! I reproduced the error you describe with the following script, which causes LightGBM to crash:

import lightgbm as lgb
from sklearn.datasets import load_breast_cancer

X_train, y_train = load_breast_cancer(return_X_y=True)
train_data = lgb.Dataset(X_train, label=y_train)
params = {
    "objective": "binary",
    "linear_tree": True,
    "min_sum_hessian": 5000
}
bst = lgb.train(params, train_data, num_boost_round=5, valid_sets={train_data}, valid_names='train')
bst.predict(X_train)

Your fix looks good to me!

[jameslamb]

Thanks for the fix @mjmckp !

[github-actions]

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