# optuna.trial.Trial¶

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

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.

None

Methods

 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. 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: Optional[datetime.datetime]

Return start datetime.

Datetime where the Trial started.

property distributions: Dict[str, optuna.distributions.BaseDistribution]

Return distributions of parameters to be optimized.

A dictionary containing all distributions.

property number: int

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

A trial number.

property params: Dict[str, Any]

Return parameters to be optimized.

A dictionary containing all parameters.

report(value, step)[源代码]

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.

Please refer to BasePruner.

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.

Report intermediate scores of SGDClassifier training.

import numpy as np
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split

import optuna

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)


• value (float) – A value returned from the objective function.

• step (int) – 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.

NotImplementedError – If trial is being used for multi-objective optimization.

None

set_system_attr(key, value)[源代码]

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.

• key (str) – A key string of the attribute.

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

None

set_user_attr(key, value)[源代码]

Set user attributes to the trial.

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

Save fixed hyperparameters of neural network training.

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

import optuna

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


• key (str) – A key string of the attribute.

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

None

should_prune()[源代码]

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.

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.

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

A boolean value. If True, the trial should be pruned according to the configured pruning algorithm. Otherwise, the trial should continue.

NotImplementedError – If trial is being used for multi-objective optimization.

bool

suggest_categorical(name, choices)[源代码]

Suggest a value for the categorical parameter.

The value is sampled from choices.

Suggest a kernel function of SVC.

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

import optuna

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)


• name (str) – A parameter name.

• choices (Sequence[Union[None, bool, int, float, str]]) – Parameter value candidates.

Union[None, bool, int, float, str]

A suggested value.

Union[None, bool, int, float, str]

suggest_discrete_uniform(name, low, high, q)[源代码]

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.

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

import numpy as np
from sklearn.model_selection import train_test_split

import optuna

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.fit(X_train, y_train)
return clf.score(X_valid, y_valid)

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


• name (str) – A parameter name.

• low (float) – Lower endpoint of the range of suggested values. low is included in the range.

• high (float) – Upper endpoint of the range of suggested values. high is included in the range.

• q (float) – A step of discretization.

A suggested float value.

float

suggest_float(name, low, high, *, step=None, log=False)[源代码]

Suggest a value for the floating point parameter.

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

1.3.0 新版功能.

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

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

import optuna

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)


• name (str) – A parameter name.

• low (float) – Lower endpoint of the range of suggested values. low is included in the range.

• high (float) –

Upper endpoint of the range of suggested values. high is excluded from the range.

注解

If step is specified, high is included as well as low because this method falls back to suggest_discrete_uniform().

• step (Optional[float]) –

A step of discretization.

注解

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 (bool) –

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().

注解

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.

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

A suggested float value.

float

suggest_int(name, low, high, step=1, log=False)[源代码]

Suggest a value for the integer parameter.

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

Suggest the number of trees in RandomForestClassifier.

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import optuna

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)


• name (str) – A parameter name.

• low (int) – Lower endpoint of the range of suggested values. low is included in the range.

• high (int) – Upper endpoint of the range of suggested values. high is included in the range.

• step (int) –

A step of discretization.

注解

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.

注解

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.

注解

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 (bool) –

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

注解

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.

注解

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.

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

int

suggest_loguniform(name, low, high)[源代码]

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.

Suggest penalty parameter C of SVC.

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

import optuna

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)


• name (str) – A parameter name.

• low (float) – Lower endpoint of the range of suggested values. low is included in the range.

• high (float) – Upper endpoint of the range of suggested values. high is excluded from the range.

A suggested float value.

float

suggest_uniform(name, low, high)[源代码]

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.

Suggest a momentum for neural network training.

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

import optuna

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)


• name (str) – A parameter name.

• low (float) – Lower endpoint of the range of suggested values. low is included in the range.

• high (float) – Upper endpoint of the range of suggested values. high is excluded from the range.

A suggested float value.

float

property system_attrs: Dict[str, Any]

Return system attributes.

A dictionary containing all system attributes.

property user_attrs: Dict[str, Any]

Return user attributes.

A dictionary containing all user attributes.