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)[source]¶

Report an objective function value for a given step.

The reported values are used by the pruners to determine whether this trial should be pruned.

See also

Please refer to BasePruner.

Note

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.

import optuna
from sklearn.linear_model import SGDClassifier

def objective(trial):
    clf = SGDClassifier(random_state=0)
    for step in range(100):
        clf.partial_fit(X_train, y_train, np.unique(y))
        intermediate_value = clf.score(X_test, y_test)
        trial.report(intermediate_value, step=step)
        if trial.should_prune():
            raise TrialPruned()

    return clf.score(X_test, y_test)

study = optuna.create_study()
study.optimize(objective, n_trials=3)

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.

import optuna
from sklearn.neural_network import MLPClassifier

def objective(trial):
    trial.set_user_attr('BATCHSIZE', 128)
    momentum = trial.suggest_uniform('momentum', 0, 1.0)
    clf = MLPClassifier(hidden_layer_sizes=(100, 50),
                        batch_size=trial.user_attrs['BATCHSIZE'],
                        momentum=momentum, solver='sgd', random_state=0)
    clf.fit(X_train, y_train)

    return clf.score(X_test, y_test)

study = optuna.create_study()
study.optimize(objective, n_trials=3)
assert 'BATCHSIZE' in study.best_trial.user_attrs.keys()
assert 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

CategoricalDistribution.

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.

import optuna
from sklearn.ensemble import GradientBoostingClassifier

def objective(trial):
    subsample = trial.suggest_discrete_uniform('subsample', 0.1, 1.0, 0.1)
    clf = GradientBoostingClassifier(subsample=subsample, random_state=0)
    clf.fit(X_train, y_train)
    return clf.score(X_test, y_test)

study = optuna.create_study()
study.optimize(objective, n_trials=3)

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.

import optuna
from sklearn.ensemble import RandomForestClassifier

def objective(trial):
    n_estimators = trial.suggest_int('n_estimators', 50, 400)
    clf = RandomForestClassifier(n_estimators=n_estimators, random_state=0)
    clf.fit(X_train, y_train)
    return clf.score(X_test, y_test)

study = optuna.create_study()
study.optimize(objective, n_trials=3)

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.

import optuna
from sklearn.svm import SVC

def objective(trial):
    c = trial.suggest_loguniform('c', 1e-5, 1e2)
    clf = SVC(C=c, gamma='scale', random_state=0)
    clf.fit(X_train, y_train)
    return clf.score(X_test, y_test)

study = optuna.create_study()
study.optimize(objective, n_trials=3)

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 momentum for neural network training.

import optuna
from sklearn.neural_network import MLPClassifier

def objective(trial):
    momentum = trial.suggest_uniform('momentum', 0.0, 1.0)
    clf = MLPClassifier(hidden_layer_sizes=(100, 50), momentum=momentum,
                        solver='sgd', random_state=0)
    clf.fit(X_train, y_train)

    return clf.score(X_test, y_test)

study = optuna.create_study()
study.optimize(objective, n_trials=3)

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.

import optuna

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

assert objective(optuna.trial.FixedTrial({'x': 1, 'y': 0})) == 1

Note

Please refer to Trial for details of methods and properties.

Parameters:	params – A dictionary containing all parameters.