optuna.pruners.ThresholdPruner

class optuna.pruners.ThresholdPruner(lower=None, upper=None, n_warmup_steps=0, interval_steps=1)[source]

Pruner to detect outlying metrics of the trials.

Prune if a metric exceeds upper threshold, falls behind lower threshold or reaches `nan`.

Example

```from optuna import create_study
from optuna.pruners import ThresholdPruner
from optuna import TrialPruned

def objective_for_upper(trial):
for step, y in enumerate(ys_for_upper):
trial.report(y, step)

if trial.should_prune():
raise TrialPruned()
return ys_for_upper[-1]

def objective_for_lower(trial):
for step, y in enumerate(ys_for_lower):
trial.report(y, step)

if trial.should_prune():
raise TrialPruned()
return ys_for_lower[-1]

ys_for_upper = [0.0, 0.1, 0.2, 0.5, 1.2]
ys_for_lower = [100.0, 90.0, 0.1, 0.0, -1]

study = create_study(pruner=ThresholdPruner(upper=1.0))
study.optimize(objective_for_upper, n_trials=10)

study = create_study(pruner=ThresholdPruner(lower=0.0))
study.optimize(objective_for_lower, n_trials=10)
```
Parameters
• lower (Optional[float]) – A minimum value which determines whether pruner prunes or not. If an intermediate value is smaller than lower, it prunes.

• upper (Optional[float]) – A maximum value which determines whether pruner prunes or not. If an intermediate value is larger than upper, it prunes.

• n_warmup_steps (int) – Pruning is disabled if the step is less than the given number of warmup steps.

• interval_steps (int) – Interval in number of steps between the pruning checks, offset by the warmup steps. If no value has been reported at the time of a pruning check, that particular check will be postponed until a value is reported. Value must be at least 1.

Methods

 `prune`(study, trial) Judge whether the trial should be pruned based on the reported values.
prune(study, trial)[source]

Judge whether the trial should be pruned based on the reported values.

Note that this method is not supposed to be called by library users. Instead, `optuna.trial.Trial.report()` and `optuna.trial.Trial.should_prune()` provide user interfaces to implement pruning mechanism in an objective function.

Parameters
• study (Study) – Study object of the target study.

• trial (FrozenTrial) – FrozenTrial object of the target trial. Take a copy before modifying this object.

Returns

A boolean value representing whether the trial should be pruned.

Return type

bool