Source code for optuna.pruners.threshold

import math
from typing import Any
from typing import Optional

import optuna
from optuna.pruners import BasePruner
from optuna.pruners.percentile import _is_first_in_interval_step

def _check_value(value: Any) -> float:
        # For convenience, we allow users to report a value that can be cast to `float`.
        value = float(value)
    except (TypeError, ValueError):
        message = "The `value` argument is of type '{}' but supposed to be a float.".format(
        raise TypeError(message)

    return value

[docs]class ThresholdPruner(BasePruner): """Pruner to detect outlying metrics of the trials. Prune if a metric exceeds upper threshold, falls behind lower threshold or reaches ``nan``. Example: .. testcode:: from optuna import create_study from optuna.exceptions import TrialPruned from optuna.pruners import ThresholdPruner def objective_for_upper(trial): for step, y in enumerate(ys_for_upper):, 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):, 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] n_trial_step = 5 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) Args lower: A minimum value which determines whether pruner prunes or not. If an intermediate value is smaller than lower, it prunes. upper: A maximum value which determines whether pruner prunes or not. If an intermediate value is larger than upper, it prunes. n_warmup_steps: Pruning is disabled until the trial exceeds the given number of step. interval_steps: 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. """ def __init__( self, lower: Optional[float] = None, upper: Optional[float] = None, n_warmup_steps: int = 0, interval_steps: int = 1, ) -> None: if lower is None and upper is None: raise TypeError("Either lower or upper must be specified.") if lower is not None: lower = _check_value(lower) if upper is not None: upper = _check_value(upper) lower = lower if lower is not None else -float("inf") upper = upper if upper is not None else float("inf") if lower > upper: raise ValueError("lower should be smaller than upper.") if n_warmup_steps < 0: raise ValueError( "Number of warmup steps cannot be negative but got {}.".format(n_warmup_steps) ) if interval_steps < 1: raise ValueError( "Pruning interval steps must be at least 1 but got {}.".format(interval_steps) ) self._lower = lower self._upper = upper self._n_warmup_steps = n_warmup_steps self._interval_steps = interval_steps def prune(self, study: "", trial: optuna.trial.FrozenTrial) -> bool: step = trial.last_step if step is None: return False n_warmup_steps = self._n_warmup_steps if step <= n_warmup_steps: return False if not _is_first_in_interval_step( step, trial.intermediate_values.keys(), n_warmup_steps, self._interval_steps ): return False latest_value = trial.intermediate_values[step] if math.isnan(latest_value): return True if latest_value < self._lower: return True if latest_value > self._upper: return True return False