Integration

class optuna.integration.ChainerPruningExtension(trial, observation_key, pruner_trigger)

Chainer extension to prune unpromising trials.

Example

Add a pruning extension which observes validation losses to Chainer Trainer.

trainer.extend(
    ChainerPruningExtension(trial, 'validation/main/loss', (1, 'epoch')))

Parameters:

• trial – A Trial corresponding to the current evaluation of the objective function.
• observation_key – An evaluation metric for pruning, e.g., main/loss and validation/main/accuracy. Please refer to chainer.Reporter reference for further details.
• pruner_trigger –

  A trigger to execute pruning. pruner_trigger is an instance of IntervalTrigger or ManualScheduleTrigger. IntervalTrigger can be specified by a tuple of the interval length and its unit like (1, 'epoch').

class optuna.integration.ChainerMNStudy(study, comm)

A wrapper of Study to incorporate Optuna with ChainerMN.

See also

ChainerMNStudy provides the same interface as Study. Please refer to optuna.study.Study for further details.

Example

Optimize an objective function that trains neural network written with ChainerMN.

comm = chainermn.create_communicator('naive')
study = optuna.load_study(study_name, storage_url)
chainermn_study = optuna.integration.ChainerMNStudy(study, comm)
chainermn_study.optimize(objective, n_trials=25)

Parameters:

• study – A Study object.
• comm – A ChainerMN communicator.

optimize(func, n_trials=None, timeout=None, catch=(<class 'Exception'>, ))

Optimize an objective function.

This method provides the same interface as optuna.study.Study.optimize() except the absence of n_jobs argument.

class optuna.integration.KerasPruningCallback(trial, monitor)

Keras callback to prune unpromising trials.

Example

Add a pruning callback which observes validation losses.

model.fit(X, y, callbacks=KerasPruningCallback(trial, 'val_loss'))

Parameters:

• trial – A Trial corresponding to the current evaluation of the objective function.
• monitor – An evaluation metric for pruning, e.g., val_loss and val_acc. Please refer to keras.Callback reference for further details.

class optuna.integration.LightGBMPruningCallback(trial, metric, valid_name='valid_0')

Callback for LightGBM to prune unpromising trials.

Example

Add a pruning callback which observes validation scores to training of a LightGBM model.

param = {'objective': 'binary', 'metric': 'binary_error'}
pruning_callback = LightGBMPruningCallback(trial, 'binary_error')
gbm = lgb.train(param, dtrain, valid_sets=[dtest], callbacks=[pruning_callback])

Parameters:

• trial – A Trial corresponding to the current evaluation of the objective function.
• metric – An evaluation metric for pruning, e.g., binary_error and multi_error. Please refer to LightGBM reference for further details.
• valid_name – The name of the target validation. Validation names are specified by valid_names option of train method. If omitted, valid_0 is used which is the default name of the first validation. Note that this argument will be ignored if you are calling cv method instead of train method.

class optuna.integration.MXNetPruningCallback(trial, eval_metric)

MXNet callback to prune unpromising trials.

Example

Add a pruning callback which observes validation accuracy.

model.fit(train_data=X, eval_data=Y,
          eval_end_callback=MXNetPruningCallback(trial, eval_metric='accuracy'))

Parameters:

• trial – A Trial corresponding to the current evaluation of the objective function.
• eval_metric – An evaluation metric name for pruning, e.g., cross-entropy and accuracy. If using default metrics like mxnet.metrics.Accuracy, use it’s default metric name. For custom metrics, use the metric_name provided to constructor. Please refer to mxnet.metrics reference for further details.

class optuna.integration.TensorFlowPruningHook(trial, estimator, metric, run_every_steps, is_higher_better=None)

TensorFlow SessionRunHook to prune unpromising trials.

Example

Add a pruning SessionRunHook for a TensorFlow’s Estimator.

pruning_hook = TensorFlowPruningHook(
    trial=trial,
    estimator=clf,
    metric="accuracy",
    is_higher_better=True,
    run_every_steps=10,
)
hooks = [pruning_hook]
tf.estimator.train_and_evaluate(
    clf,
    tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=500, hooks=hooks),
    eval_spec
)

Parameters:

• trial – A Trial corresponding to the current evaluation of the objective function.
• estimator – An estimator which you will use.
• metric – An evaluation metric for pruning, e.g., accuracy and loss.
• run_every_steps – An interval to watch the summary file.
• is_higher_better – Please do not use this argument because this class refers to StudyDirection to check whether the current study is minimize or maximize.

class optuna.integration.XGBoostPruningCallback(trial, observation_key)

Callback for XGBoost to prune unpromising trials.

Example

Add a pruning callback which observes validation errors to training of an XGBoost model.

pruning_callback = XGBoostPruningCallback(trial, 'validation-error')
bst = xgb.train(param, dtrain, evals=[(dtest, 'validation')],
                callbacks=[pruning_callback])

Parameters:

• trial – A Trial corresponding to the current evaluation of the objective function.
• observation_key – An evaluation metric for pruning, e.g., validation-error and validation-merror. Please refer to eval_metric in XGBoost reference for further details.