Trial

class optuna.trial.Trial(study, trial_id)[source]

A trial is a process of evaluating an objective function.

This object is passed to an objective function and provides interfaces to get parameter suggestion, manage the trial’s state, and set/get user-defined attributes of the trial.

Note that the direct use of this constructor is not recommended. This object is seamlessly instantiated and passed to the objective function behind the optuna.study.Study.optimize() method; hence library users do not care about instantiation of this object.

Parameters:
  • study – A Study object.
  • trial_id – A trial ID that is automatically generated.
datetime_start

Return start datetime.

Returns:Datetime where the Trial started.
distributions

Return distributions of parameters to be optimized.

Returns:A dictionary containing all distributions.
number

Return trial’s number which is consecutive and unique in a study.

Returns:A trial number.
params

Return parameters to be optimized.

Returns:A dictionary containing all parameters.
report(value, step=None)[source]

Report an objective function value.

If step is set to None, the value is stored as a final value of the trial. Otherwise, it is saved as an intermediate value.

Note that the reported value is converted to float type by applying float() function internally. Thus, it accepts all float-like types (e.g., numpy.float32). If the conversion fails, a TypeError is raised.

Example

Report intermediate scores of SGDClassifier training

>>> def objective(trial):
>>>     ...
>>>     clf = sklearn.linear_model.SGDClassifier()
>>>     for step in range(100):
>>>         clf.partial_fit(x_train , y_train , classes)
>>>         intermediate_value = clf.score(x_val , y_val)
>>>         trial.report(intermediate_value , step=step)
>>>         if trial.should_prune():
>>>             raise TrialPruned()
>>>     ...
Parameters:
  • value – A value returned from the objective function.
  • step – Step of the trial (e.g., Epoch of neural network training).
set_user_attr(key, value)[source]

Set user attributes to the trial.

The user attributes in the trial can be access via optuna.trial.Trial.user_attrs().

Example

Save fixed hyperparameters of neural network training:

>>> def objective(trial):
>>>     ...
>>>     trial.set_user_attr('BATCHSIZE', 128)
>>>
>>> study.best_trial.user_attrs
{'BATCHSIZE': 128}
Parameters:
  • key – A key string of the attribute.
  • value – A value of the attribute. The value should be JSON serializable.
should_prune(step=None)[source]

Suggest whether the trial should be pruned or not.

The suggestion is made by a pruning algorithm associated with the trial and is based on previously reported values. The algorithm can be specified when constructing a Study.

Note

If no values have been reported, the algorithm cannot make meaningful suggestions. Similarly, if this method is called multiple times with the exact same set of reported values, the suggestions will be the same.

See also

Please refer to the example code in optuna.trial.Trial.report().

Parameters:step – Deprecated since 0.12.0: Step of the trial (e.g., epoch of neural network training). Deprecated in favor of always considering the most recent step.
Returns:A boolean value. If True, the trial should be pruned according to the configured pruning algorithm. Otherwise, the trial should continue.
suggest_categorical(name, choices)[source]

Suggest a value for the categorical parameter.

The value is sampled from choices.

Example

Suggest a kernel function of SVC.

>>> def objective(trial):
>>>     ...
>>>     kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf'])
>>>     clf = sklearn.svm.SVC(kernel=kernel)
>>>     ...
Parameters:
  • name – A parameter name.
  • choices – Candidates of parameter values.
Returns:

A suggested value.

suggest_discrete_uniform(name, low, high, q)[source]

Suggest a value for the discrete parameter.

The value is sampled from the range \([\mathsf{low}, \mathsf{high}]\), and the step of discretization is \(q\). More specifically, this method returns one of the values in the sequence \(\mathsf{low}, \mathsf{low} + q, \mathsf{low} + 2 q, \dots, \mathsf{low} + k q \le \mathsf{high}\), where \(k\) denotes an integer. Note that \(high\) may be changed due to round-off errors if \(q\) is not an integer. Please check warning messages to find the changed values.

Example

Suggest a fraction of samples used for fitting the individual learners of GradientBoostingClassifier.

>>> def objective(trial):
>>>     ...
>>>     subsample = trial.suggest_discrete_uniform('subsample', 0.1, 1.0, 0.1)
>>>     clf = sklearn.ensemble.GradientBoostingClassifier(subsample=subsample)
>>>     ...
Parameters:
  • name – A parameter name.
  • low – Lower endpoint of the range of suggested values. low is included in the range.
  • high – Upper endpoint of the range of suggested values. high is included in the range.
  • q – A step of discretization.
Returns:

A suggested float value.

suggest_int(name, low, high)[source]

Suggest a value for the integer parameter.

The value is sampled from the integers in \([\mathsf{low}, \mathsf{high}]\).

Example

Suggest the number of trees in RandomForestClassifier.

>>> def objective(trial):
>>>     ...
>>>     n_estimators = trial.suggest_int('n_estimators', 50, 400)
>>>     clf = sklearn.ensemble.RandomForestClassifier(n_estimators=n_estimators)
>>>     ...
Parameters:
  • name – A parameter name.
  • low – Lower endpoint of the range of suggested values. low is included in the range.
  • high – Upper endpoint of the range of suggested values. high is included in the range.
Returns:

A suggested integer value.

suggest_loguniform(name, low, high)[source]

Suggest a value for the continuous parameter.

The value is sampled from the range \([\mathsf{low}, \mathsf{high})\) in the log domain. When \(\mathsf{low} = \mathsf{high}\), the value of \(\mathsf{low}\) will be returned.

Example

Suggest penalty parameter C of SVC.

>>> def objective(trial):
>>>     ...
>>>     c = trial.suggest_loguniform('c', 1e-5, 1e2)
>>>     clf = sklearn.svm.SVC(C=c)
>>>     ...
Parameters:
  • name – A parameter name.
  • low – Lower endpoint of the range of suggested values. low is included in the range.
  • high – Upper endpoint of the range of suggested values. high is excluded from the range.
Returns:

A suggested float value.

suggest_uniform(name, low, high)[source]

Suggest a value for the continuous parameter.

The value is sampled from the range \([\mathsf{low}, \mathsf{high})\) in the linear domain. When \(\mathsf{low} = \mathsf{high}\), the value of \(\mathsf{low}\) will be returned.

Example

Suggest a dropout rate for neural network training.

>>> def objective(trial):
>>>     ...
>>>     dropout_rate = trial.suggest_uniform('dropout_rate', 0, 1.0)
>>>     ...
Parameters:
  • name – A parameter name.
  • low – Lower endpoint of the range of suggested values. low is included in the range.
  • high – Upper endpoint of the range of suggested values. high is excluded from the range.
Returns:

A suggested float value.

user_attrs

Return user attributes.

Returns:A dictionary containing all user attributes.
class optuna.trial.FixedTrial(params)[source]

A trial class which suggests a fixed value for each parameter.

This object has the same methods as Trial, and it suggests pre-defined parameter values. The parameter values can be determined at the construction of the FixedTrial object. In contrast to Trial, FixedTrial does not depend on Study, and it is useful for deploying optimization results.

Example

Evaluate an objective function with parameter values given by a user:

>>> def objective(trial):
>>>     x = trial.suggest_uniform('x', -100, 100)
>>>     y = trial.suggest_categorical('y', [-1, 0, 1])
>>>     return x ** 2 + y
>>>
>>> objective(FixedTrial({'x': 1, 'y': 0}))
1

Note

Please refer to Trial for details of methods and properties.

Parameters:params – A dictionary containing all parameters.