Note
Click here to download the full example code
Specify Hyperparameters Manually
It’s natural that you have some specific sets of hyperparameters to try first such as initial learning rate values and the number of leaves. Also, it’s possible that you’ve already tried those sets before having Optuna find better sets of hyperparameters.
Optuna provides two APIs to support such cases:
Passing those sets of hyperparameters and let Optuna evaluate them -
enqueue_trial()Adding the results of those sets as completed
Trials -add_trial()
First Scenario: Have Optuna evaluate your hyperparameters
In this scenario, let’s assume you have some out-of-box sets of hyperparameters but have not evaluated them yet and decided to use Optuna to find better sets of hyperparameters.
Optuna has optuna.study.Study.enqueue_trial() which lets you pass those sets of
hyperparameters to Optuna and Optuna will evaluate them.
This section walks you through how to use this lit API with LightGBM.
import lightgbm as lgb
import numpy as np
import sklearn.datasets
import sklearn.metrics
from sklearn.model_selection import train_test_split
import optuna
Define the objective function.
def objective(trial):
data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
train_x, valid_x, train_y, valid_y = train_test_split(data, target, test_size=0.25)
dtrain = lgb.Dataset(train_x, label=train_y)
dvalid = lgb.Dataset(valid_x, label=valid_y)
param = {
"objective": "binary",
"metric": "auc",
"verbosity": -1,
"boosting_type": "gbdt",
"bagging_fraction": min(trial.suggest_float("bagging_fraction", 0.4, 1.0 + 1e-12), 1),
"bagging_freq": trial.suggest_int("bagging_freq", 0, 7),
"min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
}
# Add a callback for pruning.
pruning_callback = optuna.integration.LightGBMPruningCallback(trial, "auc")
gbm = lgb.train(
param, dtrain, valid_sets=[dvalid], verbose_eval=False, callbacks=[pruning_callback]
)
preds = gbm.predict(valid_x)
pred_labels = np.rint(preds)
accuracy = sklearn.metrics.accuracy_score(valid_y, pred_labels)
return accuracy
Then, construct Study for hyperparameter optimization.
study = optuna.create_study(direction="maximize", pruner=optuna.pruners.MedianPruner())
Here, we get Optuna evaluate some sets with larger "bagging_fraq" value and
the default values.
study.enqueue_trial(
{
"bagging_fraction": 1.0,
"bagging_freq": 0,
"min_child_samples": 20,
}
)
study.enqueue_trial(
{
"bagging_fraction": 0.75,
"bagging_freq": 5,
"min_child_samples": 20,
}
)
import logging
import sys
# Add stream handler of stdout to show the messages to see Optuna works expectedly.
optuna.logging.get_logger("optuna").addHandler(logging.StreamHandler(sys.stdout))
study.optimize(objective, n_trials=100, timeout=600)
Out:
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 0 finished with value: 0.972027972027972 and parameters: {'bagging_fraction': 1.0, 'bagging_freq': 0, 'min_child_samples': 20}. Best is trial 0 with value: 0.972027972027972.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 1 finished with value: 0.986013986013986 and parameters: {'bagging_fraction': 0.75, 'bagging_freq': 5, 'min_child_samples': 20}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 2 finished with value: 0.965034965034965 and parameters: {'bagging_fraction': 0.936992772162235, 'bagging_freq': 2, 'min_child_samples': 73}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 3 finished with value: 0.958041958041958 and parameters: {'bagging_fraction': 0.4375773699667835, 'bagging_freq': 2, 'min_child_samples': 32}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 4 finished with value: 0.958041958041958 and parameters: {'bagging_fraction': 0.4772253997308611, 'bagging_freq': 6, 'min_child_samples': 75}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Trial 12 finished with value: 0.951048951048951 and parameters: {'bagging_fraction': 0.8272525511198474, 'bagging_freq': 2, 'min_child_samples': 26}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 13 finished with value: 0.986013986013986 and parameters: {'bagging_fraction': 0.9090591038561117, 'bagging_freq': 3, 'min_child_samples': 5}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Trial 22 finished with value: 0.972027972027972 and parameters: {'bagging_fraction': 0.9441462900089241, 'bagging_freq': 1, 'min_child_samples': 26}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 23 finished with value: 0.972027972027972 and parameters: {'bagging_fraction': 0.8755839510053018, 'bagging_freq': 6, 'min_child_samples': 15}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 24 finished with value: 0.9790209790209791 and parameters: {'bagging_fraction': 0.838038249231563, 'bagging_freq': 3, 'min_child_samples': 29}. Best is trial 1 with value: 0.986013986013986.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Trial 30 finished with value: 0.993006993006993 and parameters: {'bagging_fraction': 0.855652720359663, 'bagging_freq': 4, 'min_child_samples': 23}. Best is trial 30 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Trial 47 finished with value: 0.965034965034965 and parameters: {'bagging_fraction': 0.6908129220198858, 'bagging_freq': 4, 'min_child_samples': 10}. Best is trial 30 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Trial 63 finished with value: 0.993006993006993 and parameters: {'bagging_fraction': 0.895930429491951, 'bagging_freq': 0, 'min_child_samples': 24}. Best is trial 30 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 64 finished with value: 0.993006993006993 and parameters: {'bagging_fraction': 0.8973158732011663, 'bagging_freq': 1, 'min_child_samples': 25}. Best is trial 30 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Second scenario: Have Optuna utilize already evaluated hyperparameters
In this scenario, let’s assume you have some out-of-box sets of hyperparameters and you have already evaluated them but the results are not desirable so that you are thinking of using Optuna.
Optuna has optuna.study.Study.add_trial() which lets you register those results
to Optuna and then Optuna will sample hyperparameters taking them into account.
In this section, the objective is the same as the first scenario.
study = optuna.create_study(direction="maximize", pruner=optuna.pruners.MedianPruner())
study.add_trial(
optuna.trial.create_trial(
params={
"bagging_fraction": 1.0,
"bagging_freq": 0,
"min_child_samples": 20,
},
distributions={
"bagging_fraction": optuna.distributions.FloatDistribution(0.4, 1.0 + 1e-12),
"bagging_freq": optuna.distributions.IntDistribution(0, 7),
"min_child_samples": optuna.distributions.IntDistribution(5, 100),
},
value=0.94,
)
)
study.add_trial(
optuna.trial.create_trial(
params={
"bagging_fraction": 0.75,
"bagging_freq": 5,
"min_child_samples": 20,
},
distributions={
"bagging_fraction": optuna.distributions.FloatDistribution(0.4, 1.0 + 1e-12),
"bagging_freq": optuna.distributions.IntDistribution(0, 7),
"min_child_samples": optuna.distributions.IntDistribution(5, 100),
},
value=0.95,
)
)
study.optimize(objective, n_trials=100, timeout=600)
Out:
A new study created in memory with name: no-name-1db92ed3-6ec3-4ad8-9160-25236d94ccd5
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 2 finished with value: 0.9300699300699301 and parameters: {'bagging_fraction': 0.6389910739843715, 'bagging_freq': 7, 'min_child_samples': 81}. Best is trial 1 with value: 0.95.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 3 finished with value: 0.993006993006993 and parameters: {'bagging_fraction': 0.7632552914764656, 'bagging_freq': 4, 'min_child_samples': 47}. Best is trial 3 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 4 finished with value: 0.9790209790209791 and parameters: {'bagging_fraction': 0.9637519082183181, 'bagging_freq': 1, 'min_child_samples': 81}. Best is trial 3 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
Trial 5 finished with value: 0.9790209790209791 and parameters: {'bagging_fraction': 0.8450463181002006, 'bagging_freq': 6, 'min_child_samples': 29}. Best is trial 3 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Trial 21 finished with value: 0.9790209790209791 and parameters: {'bagging_fraction': 0.85780068247223, 'bagging_freq': 5, 'min_child_samples': 33}. Best is trial 3 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Trial 44 finished with value: 0.9790209790209791 and parameters: {'bagging_fraction': 0.7852717230988943, 'bagging_freq': 5, 'min_child_samples': 22}. Best is trial 3 with value: 0.993006993006993.
/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/v3.0.0-b1/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning:
'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
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Total running time of the script: ( 0 minutes 6.575 seconds)