optuna.trial.Trial

class optuna.trial.Trial(study: Study, trial_id: int)[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.

__init__(study: Study, trial_id: int)None[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(study, trial_id)

Initialize self.

report(value, step)

Report an objective function value for a given step.

set_system_attr(key, value)

Set system attributes to the trial.

set_user_attr(key, value)

Set user attributes to the trial.

should_prune()

Suggest whether the trial should be pruned or not.

suggest_categorical(name, choices)

Suggest a value for the categorical parameter.

suggest_discrete_uniform(name, low, high, q)

Suggest a value for the discrete parameter.

suggest_float(name, low, high, *[, step, log])

Suggest a value for the floating point parameter.

suggest_int(name, low, high[, step, log])

Suggest a value for the integer parameter.

suggest_loguniform(name, low, high)

Suggest a value for the continuous parameter.

suggest_uniform(name, low, high)

Suggest a value for the continuous parameter.

Attributes

datetime_start

Return start datetime.

distributions

Return distributions of parameters to be optimized.

number

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

params

Return parameters to be optimized.

system_attrs

Return system attributes.

user_attrs

Return user attributes.

property datetime_start

Return start datetime.

Returns

Datetime where the Trial started.

property distributions

Return distributions of parameters to be optimized.

Returns

A dictionary containing all distributions.

property number

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

Returns

A trial number.

property params

Return parameters to be optimized.

Returns

A dictionary containing all parameters.

report(value: float, step: int)None[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 numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)

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_valid, y_valid)
        trial.report(intermediate_value, step=step)
        if trial.should_prune():
            raise optuna.TrialPruned()

    return clf.score(X_valid, y_valid)

study = optuna.create_study(direction='maximize')
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). Note that pruners assume that step starts at zero. For example, MedianPruner simply checks if step is less than n_warmup_steps as the warmup mechanism.

set_system_attr(key: str, value: Any)None[source]

Set system attributes to the trial.

Note that Optuna internally uses this method to save system messages such as failure reason of trials. Please use set_user_attr() to set users’ attributes.

Parameters
  • key – A key string of the attribute.

  • value – A value of the attribute. The value should be JSON serializable.

set_user_attr(key: str, value: Any)None[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 numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)

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_valid, y_valid)

study = optuna.create_study(direction='maximize')
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()bool[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().

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: str, choices: Sequence[CategoricalChoiceType]) → CategoricalChoiceType[source]

Suggest a value for the categorical parameter.

The value is sampled from choices.

Example

Suggest a kernel function of SVC.

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)

def objective(trial):
    kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf'])
    clf = SVC(kernel=kernel, gamma='scale', random_state=0)
    clf.fit(X_train, y_train)
    return clf.score(X_valid, y_valid)

study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=3)
Parameters
  • name – A parameter name.

  • choices – Parameter value candidates.

Returns

A suggested value.

suggest_discrete_uniform(name: str, low: float, high: float, q: float)float[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 numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)

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_valid, y_valid)

study = optuna.create_study(direction='maximize')
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_float(name: str, low: float, high: float, *, step: Optional[float] = None, log: bool = False)float[source]

Suggest a value for the floating point parameter.

Note that this is a wrapper method for suggest_uniform(), suggest_loguniform() and suggest_discrete_uniform().

New in version 1.3.0.

Example

Suggest a momentum, learning rate and scaling factor of learning rate for neural network training.

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)

def objective(trial):
    momentum = trial.suggest_float('momentum', 0.0, 1.0)
    learning_rate_init = trial.suggest_float('learning_rate_init',
                                             1e-5, 1e-3, log=True)
    power_t = trial.suggest_float('power_t', 0.2, 0.8, step=0.1)
    clf = MLPClassifier(hidden_layer_sizes=(100, 50), momentum=momentum,
                        learning_rate_init=learning_rate_init,
                        solver='sgd', random_state=0, power_t=power_t)
    clf.fit(X_train, y_train)

    return clf.score(X_valid, y_valid)

study = optuna.create_study(direction='maximize')
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.

  • step

    A step of discretization.

    Note

    The step and log arguments cannot be used at the same time. To set the step argument to a float number, set the log argument to False.

  • log

    A flag to sample the value from the log domain or not. If log is true, the value is sampled from the range in the log domain. Otherwise, the value is sampled from the range in the linear domain. See also suggest_uniform() and suggest_loguniform().

    Note

    The step and log arguments cannot be used at the same time. To set the log argument to True, set the step argument to None.

Raises

ValueError – If step is not None and log = True are specified.

Returns

A suggested float value.

suggest_int(name: str, low: int, high: int, step: int = 1, log: bool = False)int[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 numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)

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_valid, y_valid)

study = optuna.create_study(direction='maximize')
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.

  • step

    A step of discretization.

    Note

    Note that \(\mathsf{high}\) is modified if the range is not divisible by \(\mathsf{step}\). Please check the warning messages to find the changed values.

    Note

    The method returns one of the values in the sequence \(\mathsf{low}, \mathsf{low} + \mathsf{step}, \mathsf{low} + 2 * \mathsf{step}, \dots, \mathsf{low} + k * \mathsf{step} \le \mathsf{high}\), where \(k\) denotes an integer.

    Note

    The step != 1 and log arguments cannot be used at the same time. To set the step argument \(\mathsf{step} \ge 2\), set the log argument to False.

  • log

    A flag to sample the value from the log domain or not.

    Note

    If log is true, at first, the range of suggested values is divided into grid points of width 1. The range of suggested values is then converted to a log domain, from which a value is sampled. The uniformly sampled value is re-converted to the original domain and rounded to the nearest grid point that we just split, and the suggested value is determined. For example, if low = 2 and high = 8, then the range of suggested values is [2, 3, 4, 5, 6, 7, 8] and lower values tend to be more sampled than higher values.

    Note

    The step != 1 and log arguments cannot be used at the same time. To set the log argument to True, set the step argument to 1.

Raises

ValueError – If step != 1 and log = True are specified.

suggest_loguniform(name: str, low: float, high: float)float[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 numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)

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_valid, y_valid)

study = optuna.create_study(direction='maximize')
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: str, low: float, high: float)float[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 numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)

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_valid, y_valid)

study = optuna.create_study(direction='maximize')
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.

property system_attrs

Return system attributes.

Returns

A dictionary containing all system attributes.

property user_attrs

Return user attributes.

Returns

A dictionary containing all user attributes.