o }oiO @sNddlmZddlmZddlmZddlZddlZddlmZddlm Z ddlm Z ddl m Z dd l m Z dd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddl m!Z!e "e#Z$dZ%GdddeZ&e!j'ddddddddd,d*d+Z(dS)-) annotations)Mapping)SequenceN)Any)cast)overload) distributions)logging)convert_positional_args)deprecated_func)JSONSerializable) optuna_warn-_convert_old_distribution_to_new_distribution)BaseDistribution)CategoricalChoiceType)CategoricalDistribution)FloatDistribution)IntDistribution)_SUGGEST_INT_POSITIONAL_ARGS) BaseTrial) TrialStatez Use suggest_float{args} instead.c@seZdZdZdddddZddd Zdd!d"Zdd#d$Zdd%d&Zdd(d)Z dd*d+dd2d3Z e d4d5e j d6d7d8dd9d:Ze d4d5e j d;d7d8ddd7d8dd@dAZeedBdCdDdEd*d+ddFdGZeddJdKZeddMdKZeddOdKZeddQdKZeddSdKZeddVdKZddWdKZddXdYZddZd[Zdd]d^Ze d_dCdd`daZddbdcZddfdgZeddhdiZejddjdiZeddkdlZejddndlZddpdqZ ddrdsZ!ee e!Z"eddtduZ#e#jddvduZ#eddwdxZ$e$jddydxZ$eddzd{Z%e%jdd|d{Z%edd}d~Z&e&jddd~Z&edddZ'e'jdddZ'edddZ(edddZ)dS) FrozenTrialaStatus and results of a :class:`~optuna.trial.Trial`. An object of this class has the same methods as :class:`~optuna.trial.Trial`, but is not associated with, nor has any references to a :class:`~optuna.study.Study`. It is therefore not possible to make persistent changes to a storage from this object by itself, for instance by using :func:`~optuna.trial.FrozenTrial.set_user_attr`. It will suggest the parameter values stored in :attr:`params` and will not sample values from any distributions. It can be passed to objective functions (see :func:`~optuna.study.Study.optimize`) and is useful for deploying optimization results. Example: Re-evaluate an objective function with parameter values optimized study. .. testcode:: import optuna def objective(trial): x = trial.suggest_float("x", -1, 1) return x**2 study = optuna.create_study() study.optimize(objective, n_trials=3) assert objective(study.best_trial) == study.best_value .. note:: Instances are mutable, despite the name. For instance, :func:`~optuna.trial.FrozenTrial.set_user_attr` will update user attributes of objects in-place. Example: Overwritten attributes. .. testcode:: import copy import datetime import optuna def objective(trial): x = trial.suggest_float("x", -1, 1) # this user attribute always differs trial.set_user_attr("evaluation time", datetime.datetime.now()) return x**2 study = optuna.create_study() study.optimize(objective, n_trials=3) best_trial = study.best_trial best_trial_copy = copy.deepcopy(best_trial) # re-evaluate objective(best_trial) # the user attribute is overwritten by re-evaluation assert best_trial.user_attrs != best_trial_copy.user_attrs .. note:: Please refer to :class:`~optuna.trial.Trial` for details of methods and properties. Attributes: number: Unique and consecutive number of :class:`~optuna.trial.Trial` for each :class:`~optuna.study.Study`. Note that this field uses zero-based numbering. state: :class:`TrialState` of the :class:`~optuna.trial.Trial`. value: Objective value of the :class:`~optuna.trial.Trial`. ``value`` and ``values`` must not be specified at the same time. values: Sequence of objective values of the :class:`~optuna.trial.Trial`. The length is greater than 1 if the problem is multi-objective optimization. ``value`` and ``values`` must not be specified at the same time. datetime_start: Datetime where the :class:`~optuna.trial.Trial` started. datetime_complete: Datetime where the :class:`~optuna.trial.Trial` finished. params: Dictionary that contains suggested parameters. distributions: Dictionary that contains the distributions of :attr:`params`. user_attrs: Dictionary that contains the attributes of the :class:`~optuna.trial.Trial` set with :func:`optuna.trial.Trial.set_user_attr`. system_attrs: Dictionary that contains the attributes of the :class:`~optuna.trial.Trial` set with :func:`optuna.trial.Trial.set_system_attr`. intermediate_values: Intermediate objective values set with :func:`optuna.trial.Trial.report`. N)valuesnumberintstatervalue float | Nonedatetime_startdatetime.datetime | Nonedatetime_completeparamsdict[str, Any]rdict[str, BaseDistribution] user_attrs system_attrsintermediate_valuesdict[int, float]trial_idrSequence[float] | NonereturnNonec  Cs||_||_d|_|dur| durtd|dur|g|_n | dur't| |_||_||_||_||_| |_ | |_ ||_ | |_ dS)Nz)Specify only one of `value` and `values`.) _numberr_values ValueErrorlist_datetime_startr!_params _user_attrs _system_attrsr'_distributions _trial_id) selfrrrrr!r"rr%r&r'r)rr8H/home/ubuntu/.local/lib/python3.10/site-packages/optuna/trial/_frozen.py__init__s"   zFrozenTrial.__init__otherrboolcCst|tstS|j|jkSN) isinstancerNotImplemented__dict__r7r;r8r8r9__eq__s  zFrozenTrial.__eq__cCst|tstS|j|jkSr=r>rr?rrAr8r8r9__lt__  zFrozenTrial.__lt__cCst|tstS|j|jkSr=rCrAr8r8r9__le__rEzFrozenTrial.__le__csttfddjDS)Nc3s|]}t|VqdSr=)getattr.0fieldr7r8r9 z'FrozenTrial.__hash__..)hashtupler@rKr8rKr9__hash__szFrozenTrial.__hash__strcs,djjjdfddjDddS)Nz{cls}({kwargs})z, c3s>|]}dj|ds |n|ddtt|dVqdS)z{field}={value}_N)rJr)format startswithreprrGrHrKr8r9rLs  z'FrozenTrial.__repr__..z , value=None)clskwargs)rT __class____name__joinr@rKr8rKr9__repr__szFrozenTrial.__repr__F)steplognamelowfloathighr]r^c Cs||t||||dSN)r^r])_suggestrr7r_r`rbr]r^r8r8r9 suggest_floats zFrozenTrial.suggest_floatz3.0.0z6.0.0)args)textcCs||||Sr=rfr7r_r`rbr8r8r9suggest_uniformzFrozenTrial.suggest_uniformz(..., log=True)cCs|j|||ddS)NT)r^rjrkr8r8r9suggest_loguniformzFrozenTrial.suggest_loguniformz(..., step=...)qcCs|j||||dS)N)r]rj)r7r_r`rbrpr8r8r9suggest_discrete_uniformroz$FrozenTrial.suggest_discrete_uniformz3.5.0z5.0.0)previous_positional_arg_namesdeprecated_versionremoved_versionrSc Cst||t||||dSrc)rrdrrer8r8r9 suggest_intszFrozenTrial.suggest_intchoicesSequence[None]cCdSr=r8r7r_rvr8r8r9suggest_categoricalzFrozenTrial.suggest_categoricalSequence[bool]cCrxr=r8ryr8r8r9rzr{ Sequence[int]cCrxr=r8ryr8r8r9rzr{Sequence[float]cCrxr=r8ryr8r8r9rzr{ Sequence[str]cCrxr=r8ryr8r8r9rzr{Sequence[CategoricalChoiceType]rcCrxr=r8ryr8r8r9rzscCs||t|dS)N)rv)rdrryr8r8r9rzscCdS)aInterface of report function. Since :class:`~optuna.trial.FrozenTrial` is not pruned, this report function does nothing. .. seealso:: Please refer to :func:`~optuna.trial.FrozenTrial.should_prune`. Args: value: A value returned from the objective function. step: Step of the trial (e.g., Epoch of neural network training). Note that pruners assume that ``step`` starts at zero. For example, :class:`~optuna.pruners.MedianPruner` simply checks if ``step`` is less than ``n_warmup_steps`` as the warmup mechanism. Nr8)r7rr]r8r8r9report szFrozenTrial.reportcCr)a'Suggest whether the trial should be pruned or not. The suggestion is always :obj:`False` regardless of a pruning algorithm. .. note:: :class:`~optuna.trial.FrozenTrial` only samples one combination of parameters. Returns: :obj:`False`. Fr8rKr8r8r9 should_prunes zFrozenTrial.should_prunekeycC||j|<dSr=r3r7rrr8r8r9 set_user_attr,szFrozenTrial.set_user_attrz3.1.0cCrr=r4rr8r8r9set_system_attr/rmzFrozenTrial.set_system_attrcCs8|jtjkr|jdurtd|jr|jdurtdn |jdur'td|jtjkr;|jdur;td|jd|jtj krX|jdurJtdt dd|jDrXtd t |j t |j krxtd t |j t |j |j D]\}}|j|}||}||std |||q}dS) NzK`datetime_start` is supposed to be set when the trial state is not waiting.z?`datetime_complete` is supposed to be set for a finished trial.zC`datetime_complete` is supposed to be None for an unfinished trial.z2values should be None for a failed trial, but got .z*values should be set for a complete trial.css|]}t|VqdSr=)mathisnan)rIxr8r8r9rLGrMz(FrozenTrial._validate..zvalues should not contain NaN.z0Inconsistent parameters {} and distributions {}.zFThe value {} of parameter '{}' isn't contained in the distribution {}.)rrWAITINGrr/ is_finishedr!FAILr.COMPLETEanysetr"keysrrTitemsto_internal_repr _contains)r7 param_name param_value distributionparam_value_in_internal_reprr8r8r9 _validate3sF         zFrozenTrial._validaterrcCsr||jvr td||j|}||}||s$td|||||jvr2t|j||||j|<|S)NzjThe value of the parameter '{}' is not found. Please set it at the construction of the FrozenTrial object.zNThe value {} of the parameter '{}' is out of the range of the distribution {}.) r2r/rTrrr r5r check_distribution_compatibility)r7r_rrrr8r8r9rd[s       zFrozenTrial._suggestcC|jSr=r-rKr8r8r9rqzFrozenTrial.numbercC ||_dSr=rr7rr8r8r9ru cCs.|jdurt|jdkrtd|jdSdS)NrSDThis attribute is not available during multi-objective optimization.rr.len RuntimeErrorrKr8r8r9rys  zFrozenTrial.valuevcCs>|jdurt|jdkrtd|dur|g|_dSd|_dS)NrSrrr7rr8r8r9rs   list[float] | NonecCrr=)r.rKr8r8r9 _get_valuesszFrozenTrial._get_valuescCs |dur t||_dSd|_dSr=)r0r.rr8r8r9 _set_valuess zFrozenTrial._set_valuescCrr=r1rKr8r8r9rrzFrozenTrial.datetime_startcCrr=rrr8r8r9rrcCrr=r2rKr8r8r9r"rzFrozenTrial.paramscCrr=r)r7r"r8r8r9r"rcCrr=r5rKr8r8r9rrzFrozenTrial.distributionscCrr=rrr8r8r9rrcCrr=rrKr8r8r9r%rzFrozenTrial.user_attrscCrr=rrr8r8r9r%rcCrr=rrKr8r8r9r&rzFrozenTrial.system_attrsMapping[str, JSONSerializable]cCsttttf||_dSr=)rdictrQrr4rr8r8r9r&s int | NonecCs t|jdkr dSt|jS)zReturn the maximum step of :attr:`intermediate_values` in the trial. Returns: The maximum step of intermediates. rN)rr'maxrrKr8r8r9 last_stepszFrozenTrial.last_stepdatetime.timedelta | NonecCs|jr |jr |j|jSdS)ziReturn the elapsed time taken to complete the trial. Returns: The duration. N)rr!rKr8r8r9durations  zFrozenTrial.duration)rrrrrrrr r!r r"r#rr$r%r#r&r#r'r(r)rrr*r+r,)r;rr+r<)r+r)r+rQ) r_rQr`rarbrar]rr^r<r+ra)r_rQr`rarbrar+ra) r_rQr`rarbrarprar+ra) r_rQr`rrbrr]rr^r<r+r)r_rQrvrwr+r,)r_rQrvr|r+r<)r_rQrvr}r+r)r_rQrvr~r+ra)r_rQrvrr+rQ)r_rQrvrr+r)rrar]rr+r,)r+r<)rrQrrr+r,)r+r,)r_rQrrr+r)rrr+r,)r+r)rrr+r,)r+r)rr*r+r,)r+r )rr r+r,)r+r#)r"r#r+r,)r+r$)rr$r+r,)rr#r+r,)rrr+r,)r+r)r+r)*rZ __module__ __qualname____doc__r:rBrDrFrPr\rfr _suggest_deprecated_msgrTrlrnrqr rrurrzrrrrrrdpropertyrsetterrrrrrr"rr%r&rrr8r8r8r9r sy "                    (                   r)rrrr"rr%r&r'rrrrrr*r"dict[str, Any] | Noner"dict[str, BaseDistribution] | Noner%r&r'dict[int, float] | Noner+c Cs|pi}|pi}dd|D}|pi}|pi}|pi}|tjkr%d}ntj}|r1|} nd} tdd||||| |||||d } | | S)aCreate a new :class:`~optuna.trial.FrozenTrial`. Example: .. testcode:: import optuna from optuna.distributions import CategoricalDistribution from optuna.distributions import FloatDistribution trial = optuna.trial.create_trial( params={"x": 1.0, "y": 0}, distributions={ "x": FloatDistribution(0, 10), "y": CategoricalDistribution([-1, 0, 1]), }, value=5.0, ) assert isinstance(trial, optuna.trial.FrozenTrial) assert trial.value == 5.0 assert trial.params == {"x": 1.0, "y": 0} .. seealso:: See :func:`~optuna.study.Study.add_trial` for how this function can be used to create a study from existing trials. .. note:: Please note that this is a low-level API. In general, trials that are passed to objective functions are created inside :func:`~optuna.study.Study.optimize`. .. note:: When ``state`` is :class:`TrialState.COMPLETE`, the following parameters are required: * ``params`` * ``distributions`` * ``value`` or ``values`` Args: state: Trial state. value: Trial objective value. Must be specified if ``state`` is :class:`TrialState.COMPLETE`. ``value`` and ``values`` must not be specified at the same time. values: Sequence of the trial objective values. The length is greater than 1 if the problem is multi-objective optimization. Must be specified if ``state`` is :class:`TrialState.COMPLETE`. ``value`` and ``values`` must not be specified at the same time. params: Dictionary with suggested parameters of the trial. distributions: Dictionary with parameter distributions of the trial. user_attrs: Dictionary with user attributes. system_attrs: Dictionary with system attributes. Should not have to be used for most users. intermediate_values: Dictionary with intermediate objective values of the trial. Returns: Created trial. cSsi|] \}}|t|qSr8r)rIrdistr8r8r9 1sz create_trial..N) rr)rrrrr!r"rr%r&r')rrrdatetimenowrrr) rrrr"rr%r&r'rr!trialr8r8r9 create_trials<N  r)rrrrrr*r"rrrr%rr&rr'rr+r)) __future__rcollections.abcrrrrtypingrrroptunarr optuna._convert_positional_argsr optuna._deprecatedr optuna._typingr optuna._warningsr optuna.distributionsrrrrrroptuna.trial._baserroptuna.trial._stater get_loggerrZ_loggerrrrrr8r8r8r9sL                     F