add optional parameter suggestion methods for Optuna trials
This commit is contained in:
Joseph Hopfmüller
@@ -1,45 +1,335 @@
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def _optional_suggest(trial, name, range_or_value, log=False, step=None, type='int'):
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from typing import Any
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# not a range
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from optuna import trial
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if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
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return range_or_value
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def install_optional_suggests():
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trial.Trial.suggest_categorical_optional = suggest_categorical_optional_wrapper
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trial.Trial.suggest_int_optional = suggest_int_optional_wrapper
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trial.Trial.suggest_float_optional = suggest_float_optional_wrapper
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def _is_listlike(obj: Any) -> bool:
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return hasattr(obj, "__iter__") and not isinstance(obj, str)
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def _optional_suggest(
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*,
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trial: trial.Trial,
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name: str,
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range_or_value: Any,
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type: str,
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log: bool = False,
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step: int | float | None = None,
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add_user: bool = False,
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force: bool = False,
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multiply: float | int = 1,
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set_new: bool = True,
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):
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"""
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Suggest a value for a parameter with more control over the process
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Parameters
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----------
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type : str
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The type of the parameter
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trial : optuna.trial.Trial
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The trial object
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name : str
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The name of the parameter
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range_or_value : Any
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The range of values or a single value
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log : bool, optional
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Whether to use a logarithmic scale, by default False
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step : int|float|None, optional
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The step size, by default None
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add_user : bool, optional
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Whether to add the suggested value to the user attributes if not added as a parameter, by default False
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force : bool, optional
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Whether to force a single value to be suggested, by default False
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multiply : float| int, optional
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A multiplier to apply to the range or value, by default 1. Ignored for type "categorical".
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set_new : bool, optional
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Whether to override the parameter if it already exists, by default True
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"""
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# value should be retrieved from trial
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if not set_new and name in trial.params:
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return trial.params[name]
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# value is not a list or tuple
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if not _is_listlike(range_or_value):
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range_or_value = (range_or_value,)
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# range with only one value
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# range with only one value
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if len(range_or_value) == 1:
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if len(range_or_value) == 1 and not force:
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if add_user:
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trial.set_user_attr(name, range_or_value[0])
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return range_or_value[0]
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return range_or_value[0]
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if type == 'int':
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step = step or 1
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return trial.suggest_int(name, *range_or_value, step=step, log=log)
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if type == 'float':
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# normal operation
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return trial.suggest_float(name, *range_or_value, step=step, log=log)
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if type == "categorical":
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if type == 'categorical':
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return trial.suggest_categorical(name, range_or_value)
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return trial.suggest_categorical(name, range_or_value)
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# multiply range
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range_or_value = tuple(multiply * x for x in range_or_value)
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#
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if len(range_or_value) > 2:
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raise UserWarning("More than two values in range, using highest and lowest")
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low = min(range_or_value)
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high = max(range_or_value)
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if type == "float":
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return trial.suggest_float(name, low, high, step=step, log=log)
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if type == "int":
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step = step or 1
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lowi = int(low)
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highi = int(high)
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if lowi != low or highi != high:
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raise ValueError(f"Range {low} to {high} (using multiplier {multiply}) is not valid for int")
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return trial.suggest_int(name, lowi, highi, step=step, log=log)
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raise ValueError(f"Unknown type: {type}")
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raise ValueError(f"Unknown type: {type}")
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def optional_suggest_categorical(trial, name, choices_or_value):
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return _optional_suggest(trial, name, choices_or_value, type='categorical')
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def optional_suggest_int(trial, name, range_or_value, step=None, log=False):
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def suggest_categorical_optional(
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return _optional_suggest(trial, name, range_or_value, step=step, log=log, type='int')
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trial: trial.Trial,
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name: str,
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choices_or_value: tuple[Any] | list[Any] | Any,
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add_user: bool = False,
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force: bool = False,
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set_new: bool = True,
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):
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"""
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Suggest a value for a categorical parameter with more control over the process
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def optional_suggest_float(trial, name, range_or_value, step=None, log=False):
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Parameters
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return _optional_suggest(trial, name, range_or_value, step=step, log=log, type='float')
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----------
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trial : optuna.trial.Trial
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The trial object
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name : str
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The name of the parameter
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choices_or_value : tuple|list|Any
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The choices or a single value
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add_user : bool, optional
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Whether to add the suggested value to the user attributes if not added as a parameter, by default False
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force : bool, optional
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Whether to suggest a single value as a parameter, by default False
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set_new : bool, optional
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Whether to override the parameter if it already exists, by default True
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"""
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return _optional_suggest(
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trial=trial, name=name, range_or_value=choices_or_value, type="categorical", add_user=add_user, force=force, set_new=set_new
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)
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def force_suggest_int(trial, name, range_or_value, step=1, log=False):
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if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
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return trial.suggest_int(name, range_or_value, range_or_value, step=step, log=log)
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return trial.suggest_int(name, *range_or_value, step=step, log=log)
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def force_suggest_float(trial, name, range_or_value, step=None, log=False):
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def suggest_int_optional(
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if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
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trial: trial.Trial,
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return trial.suggest_float(name, range_or_value, range_or_value, step=step, log=log)
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name: str,
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return trial.suggest_float(name, *range_or_value, step=step, log=log)
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range_or_value: tuple[int] | list[int] | int,
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step: int = 1,
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def force_suggest_categorical(trial, name, range_or_value):
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log: bool = False,
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if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
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add_user: bool = False,
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return trial.suggest_categorical(name, [range_or_value])
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force: bool = False,
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return trial.suggest_categorical(name, range_or_value)
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multiply: int = 1,
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set_new: bool = True,
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):
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"""
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Suggest a value for an integer parameter with more control over the process
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Parameters
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----------
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trial : optuna.trial.Trial
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The trial object
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name : str
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The name of the parameter
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range_or_value : tuple|list|int
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The range of values or a single value.
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step : int, optional
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The step size, by default 1
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log : bool, optional
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Whether to use a logarithmic scale, by default False
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add_user : bool, optional
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Whether to add the suggested value to the user attributes if not added as a parameter, by default False
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force : bool, optional
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Whether to suggest a single value as a parameter, by default False
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"""
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return _optional_suggest(
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trial=trial,
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name=name,
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range_or_value=range_or_value,
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step=step,
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log=log,
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type="int",
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add_user=add_user,
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force=force,
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multiply=multiply,
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set_new=set_new,
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)
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def suggest_float_optional(
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trial: trial.Trial,
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name: str,
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range_or_value: tuple[float] | list[float] | float,
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step: float | None = None,
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log: bool = False,
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add_user: bool = False,
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force: bool = False,
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multiply: float = 1,
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set_new: bool = True,
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):
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"""
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Suggest a value for a float parameter with more control over the process
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Parameters
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----------
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trial : optuna.trial.Trial
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The trial object
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name : str
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The name of the parameter
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range_or_value : tuple|list|float
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The range of values or a single value
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step : float|None, optional
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The step size, by default None
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log : bool, optional
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Whether to use a logarithmic scale, by default False
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add_user : bool, optional
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Whether to add the suggested value to the user attributes if not added as a parameter, by default False
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force : bool, optional
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Whether to suggest a single value as a parameter, by default False
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multiply : float, optional
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A multiplier to apply to the range or value, by default 1
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set_new : bool, optional
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Whether to override the parameter if it already exists, by default True
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"""
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return _optional_suggest(
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trial=trial,
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name=name,
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range_or_value=range_or_value,
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step=step,
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log=log,
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type="float",
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add_user=add_user,
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force=force,
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multiply=multiply,
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set_new=set_new,
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)
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def suggest_categorical_optional_wrapper(
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self: trial.Trial,
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name: str,
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choices_or_value: tuple[Any] | list[Any] | Any,
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add_user: bool = False,
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force: bool = False,
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set_new: bool = True,
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):
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"""
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Suggest a value for a categorical parameter with more control over the process
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Parameters
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----------
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name : str
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The name of the parameter
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choices_or_value : tuple|list|Any
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The choices or a single value
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add_user : bool, optional
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Whether to add the suggested value to the user attributes if not added as a parameter, by default False
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force : bool, optional
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Whether to suggest a single value as a parameter, by default False
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set_new : bool, optional
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Whether to override the parameter if it already exists, by default True
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"""
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return suggest_categorical_optional(
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trial=self, name=name, choices_or_value=choices_or_value, add_user=add_user, force=force, set_new=set_new
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)
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def suggest_int_optional_wrapper(
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self: trial.Trial,
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name: str,
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range_or_value: tuple[int] | list[int] | int,
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step: int = 1,
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log: bool = False,
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add_user: bool = False,
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force: bool = False,
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multiply: int = 1,
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set_new: bool = True,
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):
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"""
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Suggest a value for an integer parameter with more control over the process
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Parameters
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----------
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name : str
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The name of the parameter
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range_or_value : tuple|list|int
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The range of values or a single value.
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step : int, optional
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The step size, by default 1
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log : bool, optional
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Whether to use a logarithmic scale, by default False
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add_user : bool, optional
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Whether to add the suggested value to the user attributes if not added as a parameter, by default False
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force : bool, optional
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Whether to suggest a single value as a parameter, by default False
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"""
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return suggest_int_optional(
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trial=self,
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name=name,
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range_or_value=range_or_value,
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step=step,
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log=log,
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add_user=add_user,
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force=force,
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multiply=multiply,
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set_new=set_new,
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)
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def suggest_float_optional_wrapper(
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self: trial.Trial,
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name: str,
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range_or_value: tuple[float] | list[float] | float,
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step: float | None = None,
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log: bool = False,
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add_user: bool = False,
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force: bool = False,
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multiply: float = 1,
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set_new: bool = True,
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):
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"""
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Suggest a value for a float parameter with more control over the process
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Parameters
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----------
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name : str
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The name of the parameter
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range_or_value : tuple|list|float
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The range of values or a single value
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step : float|None, optional
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The step size, by default None
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log : bool, optional
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Whether to use a logarithmic scale, by default False
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add_user : bool, optional
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Whether to add the suggested value to the user attributes if not added as a parameter, by default False
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force : bool, optional
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Whether to suggest a single value as a parameter, by default False
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multiply : float, optional
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A multiplier to apply to the range or value, by default 1
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set_new : bool, optional
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Whether to override the parameter if it already exists, by default True
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"""
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return suggest_float_optional(
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trial=self,
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name=name,
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range_or_value=range_or_value,
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step=step,
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log=log,
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add_user=add_user,
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force=force,
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multiply=multiply,
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set_new=set_new,
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)
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