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@sile @g-votte @smly @toshihikoyanase @nmasahiro
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Optuna example that optimizes a classifier configuration for cancer dataset using LightGBM.
In this example, we optimize the validation accuracy of cancer detection using LightGBM.
We optimize both the choice of booster model and their hyperparameters.
We have following two ways to execute this example:
(1) Execute this code directly.
$ python
(2) Execute through CLI.
$ STUDY_NAME=`optuna create-study --direction maximize --storage sqlite:///example.db`
$ optuna study optimize objective --n-trials=100 --study $STUDY_NAME \
--storage sqlite:///example.db
import lightgbm as lgb
import numpy as np
import sklearn.datasets
import sklearn.metrics
from sklearn.model_selection import train_test_split
import optuna
# FYI: Objective functions can take additional arguments
# (
def objective(trial):
data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25)
dtrain = lgb.Dataset(train_x, label=train_y)
param = {
'objective': 'binary',
'metric': 'binary_logloss',
'verbosity': -1,
'boosting_type': 'gbdt',
'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
'num_leaves': trial.suggest_int('num_leaves', 2, 256),
'feature_fraction': trial.suggest_uniform('feature_fraction', 0.4, 1.0),
'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.4, 1.0),
'bagging_freq': trial.suggest_int('bagging_freq', 1, 7),
'min_child_samples': trial.suggest_int('min_child_samples', 5, 100),
gbm = lgb.train(param, dtrain)
preds = gbm.predict(test_x)
pred_labels = np.rint(preds)
accuracy = sklearn.metrics.accuracy_score(test_y, pred_labels)
return accuracy
if __name__ == '__main__':
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)
print('Number of finished trials: {}'.format(len(study.trials)))
print('Best trial:')
trial = study.best_trial
print(' Value: {}'.format(trial.value))
print(' Params: ')
for key, value in trial.params.items():
print(' {}: {}'.format(key, value))
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