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.
Methods
__init__
(study, trial_id)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
Return start datetime.
Return distributions of parameters to be optimized.
Return trial's number which is consecutive and unique in a study.
Return parameters to be optimized.
Return system attributes.
Return user attributes.
- 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 applyingfloat()
function internally. Thus, it accepts all float-like types (e.g.,numpy.float32
). If the conversion fails, aTypeError
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 ifstep
is less thann_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.
See also
- 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()
andsuggest_discrete_uniform()
.New in version 1.3.0.
See also
Please see also
suggest_uniform()
,suggest_loguniform()
andsuggest_discrete_uniform()
.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
andlog
arguments cannot be used at the same time. To set thestep
argument to a float number, set thelog
argument toFalse
.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 alsosuggest_uniform()
andsuggest_loguniform()
.Note
The
step
andlog
arguments cannot be used at the same time. To set thelog
argument toTrue
, set thestep
argument toNone
.
- Raises
ValueError – If
step is not None
andlog = 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
andlog
arguments cannot be used at the same time. To set thestep
argument \(\mathsf{step} \ge 2\), set thelog
argument toFalse
.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
andlog
arguments cannot be used at the same time. To set thelog
argument toTrue
, set thestep
argument to 1.
- Raises
ValueError – If
step != 1
andlog = 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.