from typing import Union
[docs]
class ProfileOptions(dict):
"""
Options for optimization based profiling.
Attributes
----------
default_step_size:
Default step size of the profiling routine along the profile path
(adaptive step lengths algorithms will only use this as a first guess
and then refine the update).
min_step_size:
Lower bound for the step size in adaptive methods.
max_step_size:
Upper bound for the step size in adaptive methods.
step_size_factor:
Adaptive methods recompute the likelihood at the predicted point and
try to find a good step length by a sort of line search algorithm.
This factor controls step handling in this line search.
delta_ratio_max:
Maximum allowed drop of the posterior ratio between two profile steps.
ratio_min:
Lower bound for likelihood ratio of the profile, based on inverse
chi2-distribution.
The default 0.145 is slightly lower than the 95% quantile 0.1465 of a
chi2 distribution with one degree of freedom.
reg_points:
Number of profile points used for regression in regression based
adaptive profile points proposal.
reg_order:
Maximum degree of regression polynomial used in regression based
adaptive profile points proposal.
magic_factor_obj_value:
There is this magic factor in the old profiling code which slows down
profiling at small ratios (must be >= 0 and < 1).
whole_path:
Whether to profile the whole bounds or only till we get below the
ratio.
"""
[docs]
def __init__(
self,
default_step_size: float = 0.01,
min_step_size: float = 0.001,
max_step_size: float = 1.0,
step_size_factor: float = 1.25,
delta_ratio_max: float = 0.1,
ratio_min: float = 0.145,
reg_points: int = 10,
reg_order: int = 4,
magic_factor_obj_value: float = 0.5,
whole_path: bool = False,
):
super().__init__()
self.default_step_size = default_step_size
self.min_step_size = min_step_size
self.max_step_size = max_step_size
self.ratio_min = ratio_min
self.step_size_factor = step_size_factor
self.delta_ratio_max = delta_ratio_max
self.reg_points = reg_points
self.reg_order = reg_order
self.magic_factor_obj_value = magic_factor_obj_value
self.whole_path = whole_path
self.validate()
def __getattr__(self, key):
"""Allow usage of keys like attributes."""
try:
return self[key]
except KeyError:
raise AttributeError(key) from None
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
[docs]
@staticmethod
def create_instance(
maybe_options: Union["ProfileOptions", dict],
) -> "ProfileOptions":
"""
Return a valid options object.
Parameters
----------
maybe_options: ProfileOptions or dict
"""
if isinstance(maybe_options, ProfileOptions):
return maybe_options
options = ProfileOptions(**maybe_options)
return options
[docs]
def validate(self):
"""Check if options are valid.
Raises ``ValueError`` if current settings aren't valid.
"""
if self.min_step_size <= 0:
raise ValueError("min_step_size must be > 0.")
if self.max_step_size <= 0:
raise ValueError("max_step_size must be > 0.")
if self.min_step_size > self.max_step_size:
raise ValueError("min_step_size must be <= max_step_size.")
if self.default_step_size <= 0:
raise ValueError("default_step_size must be > 0.")
if self.default_step_size > self.max_step_size:
raise ValueError("default_step_size must be <= max_step_size.")
if self.default_step_size < self.min_step_size:
raise ValueError("default_step_size must be >= min_step_size.")
if self.magic_factor_obj_value < 0 or self.magic_factor_obj_value >= 1:
raise ValueError("magic_factor_obj_value must be >= 0 and < 1.")