An example of parallel execution of LightGBMTuner.

lightgbm_tuner_dump_result.py

import optuna


study = optuna.create_study(
    storage="sqlite:///lgbtuner.db", study_name="parallel", load_if_exists=True
)
study.trials_dataframe().to_csv("parallel-result.csv")

lightgbm_tuner_parallel.py

"""
Optuna example that optimizes a classifier configuration for cancer dataset using LightGBM tuner.

In this example, we optimize the validation log loss of cancer detection.

You can execute this code directly.
    $ python lightgbm_tuner_parallel.py

"""
import lightgbm as lgb
import numpy as np
import sklearn.datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

import optuna
from optuna.integration.lightgbm import LightGBMTuner


data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
train_x, val_x, train_y, val_y = train_test_split(data, target, test_size=0.25, random_state=1)
dtrain = lgb.Dataset(train_x, label=train_y)
dval = lgb.Dataset(val_x, label=val_y)

params = {
    "objective": "binary",
    "metric": "binary_logloss",
    "verbosity": -1,
    "boosting_type": "gbdt",
}

study = optuna.create_study(
    storage="sqlite:///lgbtuner.db", study_name="parallel", load_if_exists=True
)

tuner = LightGBMTuner(
    params,
    dtrain,
    valid_sets=[dtrain, dval],
    verbose_eval=100,
    early_stopping_rounds=100,
    study=study,
)
model = tuner.run()
prediction = np.rint(model.predict(val_x, num_iteration=model.best_iteration))
accuracy = accuracy_score(val_y, prediction)
best_params = model.params

print("Number of finished trials: {}".format(len(study.trials)))
print("Best params:", best_params)
print("  Accuracy = {}".format(accuracy))
print("  Params: ")
for key, value in best_params.items():
    print("    {}: {}".format(key, value))

I am running this script on the same dataset. But there seems to be an error in line 45 :

prediction = np.rint(model.predict(test_x, num_iteration=model.best_iteration))
AttributeError: 'NoneType' object has no attribute 'predict'

thanks in advance!

I also got the same problem as @shen2014 . tuner.run() returns None, hence predict attribute cannot be accessed. Using model = tuner.get_best_booster() should fix this problem.