Profile¶

class pypesto.profile.ProfileOptions(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)¶

Bases: dict

Options for optimization based profiling.

Parameters:

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 corresponds to 95% confidence
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)

__class__¶: alias of builtins.type

__contains__()¶: True if D has a key k, else False.

__delattr__¶: Delete self[key].

__delitem__¶: Delete self[key].

__dict__ = mappingproxy({'__module__': 'pypesto.profile.profile', '__doc__': '\n Options for optimization based profiling.\n\n Parameters\n ----------\n default_step_size:\n default step size of the profiling routine along the profile path\n (adaptive step lengths algorithms will only use this as a first guess\n and then refine the update)\n min_step_size:\n lower bound for the step size in adaptive methods\n max_step_size:\n upper bound for the step size in adaptive methods\n step_size_factor:\n Adaptive methods recompute the likelihood at the predicted point and\n try to find a good step length by a sort of line search algorithm.\n This factor controls step handling in this line search\n delta_ratio_max:\n maximum allowed drop of the posterior ratio between two profile steps\n ratio_min:\n lower bound for likelihood ratio of the profile, based on inverse\n chi2-distribution.\n The default corresponds to 95% confidence\n reg_points:\n number of profile points used for regression in regression based\n adaptive profile points proposal\n reg_order:\n maximum degree of regression polynomial used in regression based\n adaptive profile points proposal\n magic_factor_obj_value:\n There is this magic factor in the old profiling code which slows down\n profiling at small ratios (must be >= 0 and < 1)\n ', '__init__': <function ProfileOptions.__init__>, '__getattr__': <function ProfileOptions.__getattr__>, '__setattr__': <slot wrapper '__setitem__' of 'dict' objects>, '__delattr__': <slot wrapper '__delitem__' of 'dict' objects>, 'create_instance': <staticmethod object>, '__dict__': <attribute '__dict__' of 'ProfileOptions' objects>, '__weakref__': <attribute '__weakref__' of 'ProfileOptions' objects>})¶

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattr__(key)¶

__getattribute__¶: Return getattr(self, name).

__getitem__()¶: x.__getitem__(y) <==> x[y]

__gt__¶: Return self>value.

__hash__ = None¶

__init__(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)¶: Initialize self. See help(type(self)) for accurate signature.

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__iter__¶: Implement iter(self).

__le__¶: Return self<=value.

__len__¶: Return len(self).

__lt__¶: Return self<value.

__module__ = 'pypesto.profile.profile'¶

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Set self[key] to value.

__setitem__¶: Set self[key] to value.

__sizeof__() → size of D in memory, in bytes¶

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__¶: list of weak references to the object (if defined)

clear() → None. Remove all items from D.¶

copy() → a shallow copy of D¶

static create_instance(maybe_options: Union[ProfileOptions, Dict]) → pypesto.profile.profile.ProfileOptions¶

Returns a valid options object.

Parameters:	maybe_options (ProfileOptions or dict) –

fromkeys()¶: Returns a new dict with keys from iterable and values equal to value.

get(k[, d]) → D[k] if k in D, else d. d defaults to None.¶

items() → a set-like object providing a view on D's items¶

keys() → a set-like object providing a view on D's keys¶

pop(k[, d]) → v, remove specified key and return the corresponding value.¶: If key is not found, d is returned if given, otherwise KeyError is raised

popitem() → (k, v), remove and return some (key, value) pair as a¶: 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D¶

update([E, ]**F) → None. Update D from dict/iterable E and F.¶: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values¶

class pypesto.profile.ProfilerResult(x_path, fval_path, ratio_path, gradnorm_path=None, exitflag_path=None, time_path=None, time_total=0.0, n_fval=0, n_grad=0, n_hess=0, message=None)¶

Bases: dict

The result of a profiler run. The standardized return return value from pypesto.profile, which can either be initialized from an OptimizerResult or from an existing ProfilerResult (in order to extend the computation).

Can be used like a dict.

x_path¶

The path of the best found parameters along the profile (Dimension: n_par x n_profile_points)

Type:	ndarray

fval_path¶

The function values, fun(x), along the profile.

Type:	ndarray

ratio_path¶

The ratio of the posterior function along the profile.

Type:	ndarray

gradnorm_path¶

The gradient norm along the profile.

Type:	ndarray

exitflag_path¶

The exitflags of the optimizer along the profile.

Type:	ndarray

time_path¶

The computation time of the optimizer runs along the profile.

Type:	ndarray

time_total¶

The total computation time for the profile.

Type:	ndarray

n_fval¶

Number of function evaluations.

Type:	int

n_grad¶

Number of gradient evaluations.

Type:	int

n_hess¶

Number of Hessian evaluations.

Type:	int

message¶

Textual comment on the profile result.

Type:	str

Notes

Any field not supported by the profiler or the profiling optimizer is filled with None. Some fields are filled by pypesto itself.

__class__¶: alias of builtins.type

__contains__()¶: True if D has a key k, else False.

__delattr__¶: Delete self[key].

__delitem__¶: Delete self[key].

__dict__ = mappingproxy({'__module__': 'pypesto.profile.result', '__doc__': '\n The result of a profiler run. The standardized return return value from\n pypesto.profile, which can either be initialized from an OptimizerResult\n or from an existing ProfilerResult (in order to extend the computation).\n\n Can be used like a dict.\n\n Attributes\n ----------\n\n x_path: ndarray\n The path of the best found parameters along the profile\n (Dimension: n_par x n_profile_points)\n\n fval_path: ndarray\n The function values, fun(x), along the profile.\n\n ratio_path: ndarray\n The ratio of the posterior function along the profile.\n\n gradnorm_path: ndarray\n The gradient norm along the profile.\n\n exitflag_path: ndarray\n The exitflags of the optimizer along the profile.\n\n time_path: ndarray\n The computation time of the optimizer runs along the profile.\n\n time_total: ndarray\n The total computation time for the profile.\n\n n_fval: int\n Number of function evaluations.\n\n n_grad: int\n Number of gradient evaluations.\n\n n_hess: int\n Number of Hessian evaluations.\n\n message: str\n Textual comment on the profile result.\n\n Notes\n -----\n\n Any field not supported by the profiler or the profiling optimizer is\n filled with None. Some fields are filled by pypesto itself.\n ', '__init__': <function ProfilerResult.__init__>, '__getattr__': <function ProfilerResult.__getattr__>, '__setattr__': <slot wrapper '__setitem__' of 'dict' objects>, '__delattr__': <slot wrapper '__delitem__' of 'dict' objects>, 'append_profile_point': <function ProfilerResult.append_profile_point>, 'flip_profile': <function ProfilerResult.flip_profile>, '__dict__': <attribute '__dict__' of 'ProfilerResult' objects>, '__weakref__': <attribute '__weakref__' of 'ProfilerResult' objects>})¶

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattr__(key)¶

__getattribute__¶: Return getattr(self, name).

__getitem__()¶: x.__getitem__(y) <==> x[y]

__gt__¶: Return self>value.

__hash__ = None¶

__init__(x_path, fval_path, ratio_path, gradnorm_path=None, exitflag_path=None, time_path=None, time_total=0.0, n_fval=0, n_grad=0, n_hess=0, message=None)¶: Initialize self. See help(type(self)) for accurate signature.