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fix(tuning): cap LightGBM search space for thin datasets to prevent memorisation #321

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fix(tuning): cap LightGBM search space for thin datasets to prevent memorisation #321

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18 changes: 16 additions & 2 deletions openavmkit/tuning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,6 +121,20 @@ def _tune_lightgbm(
dict: Best hyperparameters found by Optuna.
"""

# Bound search space by training-fold size to prevent memorisation on thin datasets.
# Each CV fold trains on roughly (n_splits-1)/n_splits of the data.
n_train_per_fold = int(len(X) * (n_splits - 1) / n_splits)
# num_leaves: cap at n_train_per_fold // 4 so each leaf covers ~4+ samples on average.
# This prevents the tuner from selecting thousands of leaves from a few hundred rows.
max_num_leaves = max(8, min(2048, n_train_per_fold // 4))
# min_data_in_leaf: upper bound must not exceed training fold size or every split is illegal.
max_min_data_in_leaf = max(2, min(500, n_train_per_fold // 4))
if verbose and max_num_leaves < 64:
print(
f" [tune_lightgbm] thin dataset (n_train_per_fold={n_train_per_fold}): "
f"num_leaves capped at {max_num_leaves}, min_data_in_leaf capped at {max_min_data_in_leaf}"
)

def objective(trial):
"""Objective function for Optuna to optimize LightGBM hyperparameters."""
params = {
Expand All @@ -132,12 +146,12 @@ def objective(trial):
"learning_rate", 0.0001, 0.1, log=True
),
"max_bin": trial.suggest_int("max_bin", 64, 1024),
"num_leaves": trial.suggest_int("num_leaves", 64, 2048),
"num_leaves": trial.suggest_int("num_leaves", min(64, max_num_leaves), max_num_leaves),
"max_depth": trial.suggest_int("max_depth", 5, 15),
"min_gain_to_split": trial.suggest_float(
"min_gain_to_split", 1e-4, 50, log=True
),
"min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 20, 500),
"min_data_in_leaf": trial.suggest_int("min_data_in_leaf", min(20, max_min_data_in_leaf), max_min_data_in_leaf),
"feature_fraction": trial.suggest_float(
"feature_fraction", 0.4, 0.9, log=False
),
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