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add optional parameter suggestion methods for Optuna trials

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This commit is contained in:
Joseph Hopfmüller
2024-11-24 01:55:12 +01:00
parent 80e9a3379e
commit 9a16a5637d
1 changed files with 325 additions and 35 deletions
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360
src/single-core-regen/util/optuna_helpers.py
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@@ -1,45 +1,335 @@
def _optional_suggest(trial, name, range_or_value, log=False, step=None, type='int'):
# not a range
if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
return range_or_value
from typing import Any
from optuna import trial
def install_optional_suggests():
trial.Trial.suggest_categorical_optional = suggest_categorical_optional_wrapper
trial.Trial.suggest_int_optional = suggest_int_optional_wrapper
trial.Trial.suggest_float_optional = suggest_float_optional_wrapper
def _is_listlike(obj: Any) -> bool:
return hasattr(obj, "__iter__") and not isinstance(obj, str)
def _optional_suggest(
*,
trial: trial.Trial,
name: str,
range_or_value: Any,
type: str,
log: bool = False,
step: int | float | None = None,
add_user: bool = False,
force: bool = False,
multiply: float | int = 1,
set_new: bool = True,
):
"""
Suggest a value for a parameter with more control over the process
Parameters
----------
type : str
The type of the parameter
trial : optuna.trial.Trial
The trial object
name : str
The name of the parameter
range_or_value : Any
The range of values or a single value
log : bool, optional
Whether to use a logarithmic scale, by default False
step : int|float|None, optional
The step size, by default None
add_user : bool, optional
Whether to add the suggested value to the user attributes if not added as a parameter, by default False
force : bool, optional
Whether to force a single value to be suggested, by default False
multiply : float| int, optional
A multiplier to apply to the range or value, by default 1. Ignored for type "categorical".
set_new : bool, optional
Whether to override the parameter if it already exists, by default True
"""
# value should be retrieved from trial
if not set_new and name in trial.params:
return trial.params[name]
# value is not a list or tuple
if not _is_listlike(range_or_value):
range_or_value = (range_or_value,)
# range with only one value
if len(range_or_value) == 1:
if len(range_or_value) == 1 and not force:
if add_user:
trial.set_user_attr(name, range_or_value[0])
return range_or_value[0]
if type == 'int':
step = step or 1
return trial.suggest_int(name, *range_or_value, step=step, log=log)
if type == 'float':
return trial.suggest_float(name, *range_or_value, step=step, log=log)
if type == 'categorical':
# normal operation
if type == "categorical":
return trial.suggest_categorical(name, range_or_value)
# multiply range
range_or_value = tuple(multiply * x for x in range_or_value)
#
if len(range_or_value) > 2:
raise UserWarning("More than two values in range, using highest and lowest")
low = min(range_or_value)
high = max(range_or_value)
if type == "float":
return trial.suggest_float(name, low, high, step=step, log=log)
if type == "int":
step = step or 1
lowi = int(low)
highi = int(high)
if lowi != low or highi != high:
raise ValueError(f"Range {low} to {high} (using multiplier {multiply}) is not valid for int")
return trial.suggest_int(name, lowi, highi, step=step, log=log)
raise ValueError(f"Unknown type: {type}")
def optional_suggest_categorical(trial, name, choices_or_value):
return _optional_suggest(trial, name, choices_or_value, type='categorical')
def optional_suggest_int(trial, name, range_or_value, step=None, log=False):
return _optional_suggest(trial, name, range_or_value, step=step, log=log, type='int')
def suggest_categorical_optional(
trial: trial.Trial,
name: str,
choices_or_value: tuple[Any] | list[Any] | Any,
add_user: bool = False,
force: bool = False,
set_new: bool = True,
):
"""
Suggest a value for a categorical parameter with more control over the process
def optional_suggest_float(trial, name, range_or_value, step=None, log=False):
return _optional_suggest(trial, name, range_or_value, step=step, log=log, type='float')
Parameters
----------
trial : optuna.trial.Trial
The trial object
name : str
The name of the parameter
choices_or_value : tuple|list|Any
The choices or a single value
add_user : bool, optional
Whether to add the suggested value to the user attributes if not added as a parameter, by default False
force : bool, optional
Whether to suggest a single value as a parameter, by default False
set_new : bool, optional
Whether to override the parameter if it already exists, by default True
"""
return _optional_suggest(
trial=trial, name=name, range_or_value=choices_or_value, type="categorical", add_user=add_user, force=force, set_new=set_new
)
def force_suggest_int(trial, name, range_or_value, step=1, log=False):
if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
return trial.suggest_int(name, range_or_value, range_or_value, step=step, log=log)
return trial.suggest_int(name, *range_or_value, step=step, log=log)
def force_suggest_float(trial, name, range_or_value, step=None, log=False):
if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
return trial.suggest_float(name, range_or_value, range_or_value, step=step, log=log)
return trial.suggest_float(name, *range_or_value, step=step, log=log)
def force_suggest_categorical(trial, name, range_or_value):
if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
return trial.suggest_categorical(name, [range_or_value])
return trial.suggest_categorical(name, range_or_value)
def suggest_int_optional(
trial: trial.Trial,
name: str,
range_or_value: tuple[int] | list[int] | int,
step: int = 1,
log: bool = False,
add_user: bool = False,
force: bool = False,
multiply: int = 1,
set_new: bool = True,
):
"""
Suggest a value for an integer parameter with more control over the process
Parameters
----------
trial : optuna.trial.Trial
The trial object
name : str
The name of the parameter
range_or_value : tuple|list|int
The range of values or a single value.
step : int, optional
The step size, by default 1
log : bool, optional
Whether to use a logarithmic scale, by default False
add_user : bool, optional
Whether to add the suggested value to the user attributes if not added as a parameter, by default False
force : bool, optional
Whether to suggest a single value as a parameter, by default False
"""
return _optional_suggest(
trial=trial,
name=name,
range_or_value=range_or_value,
step=step,
log=log,
type="int",
add_user=add_user,
force=force,
multiply=multiply,
set_new=set_new,
)
def suggest_float_optional(
trial: trial.Trial,
name: str,
range_or_value: tuple[float] | list[float] | float,
step: float | None = None,
log: bool = False,
add_user: bool = False,
force: bool = False,
multiply: float = 1,
set_new: bool = True,
):
"""
Suggest a value for a float parameter with more control over the process
Parameters
----------
trial : optuna.trial.Trial
The trial object
name : str
The name of the parameter
range_or_value : tuple|list|float
The range of values or a single value
step : float|None, optional
The step size, by default None
log : bool, optional
Whether to use a logarithmic scale, by default False
add_user : bool, optional
Whether to add the suggested value to the user attributes if not added as a parameter, by default False
force : bool, optional
Whether to suggest a single value as a parameter, by default False
multiply : float, optional
A multiplier to apply to the range or value, by default 1
set_new : bool, optional
Whether to override the parameter if it already exists, by default True
"""
return _optional_suggest(
trial=trial,
name=name,
range_or_value=range_or_value,
step=step,
log=log,
type="float",
add_user=add_user,
force=force,
multiply=multiply,
set_new=set_new,
)
def suggest_categorical_optional_wrapper(
self: trial.Trial,
name: str,
choices_or_value: tuple[Any] | list[Any] | Any,
add_user: bool = False,
force: bool = False,
set_new: bool = True,
):
"""
Suggest a value for a categorical parameter with more control over the process
Parameters
----------
name : str
The name of the parameter
choices_or_value : tuple|list|Any
The choices or a single value
add_user : bool, optional
Whether to add the suggested value to the user attributes if not added as a parameter, by default False
force : bool, optional
Whether to suggest a single value as a parameter, by default False
set_new : bool, optional
Whether to override the parameter if it already exists, by default True
"""
return suggest_categorical_optional(
trial=self, name=name, choices_or_value=choices_or_value, add_user=add_user, force=force, set_new=set_new
)
def suggest_int_optional_wrapper(
self: trial.Trial,
name: str,
range_or_value: tuple[int] | list[int] | int,
step: int = 1,
log: bool = False,
add_user: bool = False,
force: bool = False,
multiply: int = 1,
set_new: bool = True,
):
"""
Suggest a value for an integer parameter with more control over the process
Parameters
----------
name : str
The name of the parameter
range_or_value : tuple|list|int
The range of values or a single value.
step : int, optional
The step size, by default 1
log : bool, optional
Whether to use a logarithmic scale, by default False
add_user : bool, optional
Whether to add the suggested value to the user attributes if not added as a parameter, by default False
force : bool, optional
Whether to suggest a single value as a parameter, by default False
"""
return suggest_int_optional(
trial=self,
name=name,
range_or_value=range_or_value,
step=step,
log=log,
add_user=add_user,
force=force,
multiply=multiply,
set_new=set_new,
)
def suggest_float_optional_wrapper(
self: trial.Trial,
name: str,
range_or_value: tuple[float] | list[float] | float,
step: float | None = None,
log: bool = False,
add_user: bool = False,
force: bool = False,
multiply: float = 1,
set_new: bool = True,
):
"""
Suggest a value for a float parameter with more control over the process
Parameters
----------
name : str
The name of the parameter
range_or_value : tuple|list|float
The range of values or a single value
step : float|None, optional
The step size, by default None
log : bool, optional
Whether to use a logarithmic scale, by default False
add_user : bool, optional
Whether to add the suggested value to the user attributes if not added as a parameter, by default False
force : bool, optional
Whether to suggest a single value as a parameter, by default False
multiply : float, optional
A multiplier to apply to the range or value, by default 1
set_new : bool, optional
Whether to override the parameter if it already exists, by default True
"""
return suggest_float_optional(
trial=self,
name=name,
range_or_value=range_or_value,
step=step,
log=log,
add_user=add_user,
force=force,
multiply=multiply,
set_new=set_new,
)