sg+ bdZddlZddlZddlmZddlmZddlmZddl m Z ddl m Z ddl Zdd lmZdd lmZdd lmZmZmZmZdd lmZdd lmZmZmZmZmZm Z m!Z!m"Z"ddl#m$Z$ddl%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?ddl@mAZAmBZBmCZCmDZDmEZEmFZFmGZGmHZHmIZIdZJGddZKGddeZLGddeLZMedeNeOdgiddZPGddeZQdZRd ZSeeOgdeTeUehd!eed"hgd#gd$dd"dd%d&ZVeVe,ZWeVe:ZXeVe1d'(ZYeVe1d'(ZZd)Z[e[eZ_\eVe6d'(Z]eVe7d'(Z^eVe2d'(Z_eVe3d'(Z`eVe8d'(ZaeVe=d'(ZbeVe>d'(ZceVe5d'(ZdeVe4d'(ZeeVe+ZfeVe&ZgeVe(ZheVe0Zid*Zjd+ZkeVejZleVekd'(ZmeVe?dd,-ZneVeebd?ecd@eddAeedBefdCegdDendEeodFesdGeqdHetdIerdJehdKepdLeudMevdNeldOemdPexdQeydRezdSe{dTe|dUe}dVe~dWedXeZdYZdZe9fd[e;fd\e-fd]e.ffD]:\ZZeVed^_ee<d`D]#ZdajeeZeVedebee<%<eedcdgeeeeOeTeeUedgd#gd#gddddkd'ddedfZdgZGdhdieLZy)laH The :mod:`sklearn.metrics.scorer` submodule implements a flexible interface for model selection and evaluation using arbitrary score functions. A scorer object is a callable that can be passed to :class:`~sklearn.model_selection.GridSearchCV` or :func:`sklearn.model_selection.cross_val_score` as the ``scoring`` parameter, to specify how a model should be evaluated. The signature of the call is ``(estimator, X, y)`` where ``estimator`` is the model to be evaluated, ``X`` is the test data and ``y`` is the ground truth labeling (or ``None`` in the case of unsupervised models). N)Counter)partial) signature)Integral) format_exc) is_regressor)Bunch) HasMethodsHidden StrOptionsvalidate_params_get_response_values)MetadataRequestMetadataRouter MethodMapping_MetadataRequester_raise_for_params_routing_enabledget_routing_for_objectprocess_routing)_check_response_method)accuracy_scoreaverage_precision_scorebalanced_accuracy_scorebrier_score_lossclass_likelihood_ratiosd2_absolute_error_scoreexplained_variance_scoref1_score jaccard_scorelog_lossmatthews_corrcoef max_errormean_absolute_errormean_absolute_percentage_errormean_gamma_deviancemean_poisson_deviancemean_squared_errormean_squared_log_errormedian_absolute_errorprecision_scorer2_score recall_score roc_auc_scoreroot_mean_squared_errorroot_mean_squared_log_errortop_k_accuracy_score) adjusted_mutual_info_scoreadjusted_rand_scorecompleteness_scorefowlkes_mallows_scorehomogeneity_scoremutual_info_scorenormalized_mutual_info_score rand_scorev_measure_scorecR| ||vr||St|g|d|i|\}}||||<|S)z/Call estimator with method and args and kwargs.response_methodr)cache estimatorr?argskwargsresult_s J/var/www/html/venv/lib/python3.12/site-packages/sklearn/metrics/_scorer.py _cached_callrGUsZ _5_%%$*9=CIFA !'o Mc4eZdZdZdddZdZdZdZdZy ) _MultimetricScoreraCallable for multimetric scoring used to avoid repeated calls to `predict_proba`, `predict`, and `decision_function`. `_MultimetricScorer` will return a dictionary of scores corresponding to the scorers in the dictionary. Note that `_MultimetricScorer` can be created with a dictionary with one key (i.e. only one actual scorer). Parameters ---------- scorers : dict Dictionary mapping names to callable scorers. raise_exc : bool, default=True Whether to raise the exception in `__call__` or not. If set to `False` a formatted string of the exception details is passed as result of the failing scorer. T) raise_excc ||_||_yN)_scorers _raise_exc)selfscorersrKs rF__init__z_MultimetricScorer.__init__ws #rHc hi}|j|rind}tt|}trt |dfi|}n.t di|j Dcic]}|t |c}}|j jD]q\}} t| tr1| j||g|i|j|j} n%| |g|i|j|j} | ||<s|Scc}w#t$r%} |jr| t||<Yd} ~ d} ~ wwxYw)z!Evaluate predicted target values.NscorerT) _use_cacherrGrrr rNitems isinstance _BaseScorer_scoregetrT ExceptionrOr) rPrArBrCscoresr@ cached_call routed_paramsnamescorerrTes rF__call__z_MultimetricScorer.__call__{s6ooi0dlE2  +D'DVDM"9=G4V,,GM!MM//1 0LD& 0fk2)FMM#Y159F9J9J49P9V9VE#9UtU}7H7H7N7T7TUE$t  0 #H 0??G#- A+D D1 D,,D1crdj|jDcgc]}d|d c}}d|dScc}w)N, "zMultiMetricScorer())joinrN)rPsrQs rF__repr__z_MultimetricScorer.__repr__s;))t}}=!q1X=>#G9A..>s 4c @t|jdk(ryt|jjDcgc]2}t |t r t ||jj4c}}td|jDryycc}w)zuReturn True if using a cache is beneficial, thus when a response method will be called several time. rFc3&K|] }|dkD yw)rNrV).0vals rF z0_MultimetricScorer._use_cache..s33sQw3sT) lenrNrvaluesrYrZr_response_method__name__any)rPrArbcounters rFrWz_MultimetricScorer._use_caches t}}  "#mm224 fk2'y&2I2IJSS   3'.."23 3 s7Bc t|jjjdi|jdt jddiS)abGet metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.3 Returns ------- routing : MetadataRouter A :class:`~utils.metadata_routing.MetadataRouter` encapsulating routing information. ownermethod_mappingrT)callercalleerV)r __class__rtaddrNrrPs rFget_metadata_routingz'_MultimetricScorer.get_metadata_routingsOA~DNN$;$;<@@ mm (?..gg.N  rHN) rt __module__ __qualname____doc__rRrdrkrWrrVrHrFrJrJds%$.2$</* rHrJc8eZdZdZd dZdZdZd dZdZdZ y) rZa>Base scorer that is used as `scorer(estimator, X, y_true)`. Parameters ---------- score_func : callable The score function to use. It will be called as `score_func(y_true, y_pred, **kwargs)`. sign : int Either 1 or -1 to returns the score with `sign * score_func(estimator, X, y)`. Thus, `sign` defined if higher scores are better or worse. kwargs : dict Additional parameters to pass to the score function. response_method : str The method to call on the estimator to get the response values. cJ||_||_||_||_d|_yrM) _score_func_sign_kwargsrs_deprecation_msg)rP score_funcsignrCr?s rFrRz_BaseScorer.__init__s(%  / $rHcd|jvr|jdSt|jj}d|vr|djSy)N pos_label)rrr parametersdefault)rPscore_func_paramss rF_get_pos_labelz_BaseScorer._get_pos_labelsQ $,, &<< , ,%d&6&67BB + +$[199 9rHc |jdkDrdnd}d|j}dj|jj Dcgc] \}}d|d|c}}}d|j j |||dScc}}w) Nrz, greater_is_better=Falsez, response_method=rf=z make_scorer(rh)rrsrirrXrrt)rP sign_stringresponse_method_stringkv kwargs_strings rFrkz_BaseScorer.__repr__s JJNb0K #5d6K6K5N!O$,,:L:L:N O$!Q2aS! OP 4++445k]%&}oQ 8 !Ps B Nc |j&tj|jtdt ||dt j |}|||d<|jttd|||fi|S)aEvaluate predicted target values for X relative to y_true. Parameters ---------- estimator : object Trained estimator to use for scoring. Must have a predict_proba method; the output of that is used to compute the score. X : {array-like, sparse matrix} Test data that will be fed to estimator.predict. y_true : array-like Gold standard target values for X. sample_weight : array-like of shape (n_samples,), default=None Sample weights. **kwargs : dict Other parameters passed to the scorer. Refer to :func:`set_score_request` for more details. Only available if `enable_metadata_routing=True`. See the :ref:`User Guide `. .. versionadded:: 1.3 Returns ------- score : float Score function applied to prediction of estimator on X. Nr)category stacklevel sample_weight) rwarningswarnDeprecationWarningrcopydeepcopyr[rrG)rPrAXy_truerrCrs rFrdz_BaseScorer.__call__s{B  , MM%%0Bq  &$---'  $'4GO $t{{7<6 1fXPWXXrHc|j tn"t|jj}|j|j}|r t j |d|t yy)zWarn if there is any overlap between ``self._kwargs`` and ``kwargs``. This method is intended to be used to check for overlap between ``self._kwargs`` and ``kwargs`` passed as metadata. Nz Overlapping parameters are: )rsetkeys intersectionrr UserWarning)rPmessagerCroverlaps rF _warn_overlapz_BaseScorer._warn_overlap"sc  <</#%S9J9J9L5M&&v{{}5  MM)8 BK  rHc ts td|jd|t|jj |_|jD],\}}|j jj||.|S)awSet requested parameters by the scorer. Please see :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.3 Parameters ---------- kwargs : dict Arguments should be of the form ``param_name=alias``, and `alias` can be one of ``{True, False, None, str}``. This method is only available when metadata routing is enabled. You can enable it using sklearn.set_config(enable_metadata_routing=True).zYou are setting metadata request for parameters which are already set as kwargs for this metric. These set values will be overridden by passed metadata if provided. Please pass them either as metadata or kwargs to `make_scorer`.rrCrxparamalias) r RuntimeErrorrrr}rt_metadata_requestrXrT add_requestrPrCrrs rFset_score_requestz_BaseScorer.set_score_request/s !E  :   "1t~~7N7N!O"LLN OLE5  " " ( ( 4 45 4 N O rH)predictrM) rtrrrrRrrkrdrrrVrHrFrZrZs'&% ,Y\ !rHrZceZdZdZy)_Scorerc |jd|t|rdn|j}t||j}||t |||}i|j |} |j|j||fi| zS)aEvaluate the response method of `estimator` on `X` and `y_true`. Parameters ---------- method_caller : callable Returns predictions given an estimator, method name, and other arguments, potentially caching results. estimator : object Trained estimator to use for scoring. X : {array-like, sparse matrix} Test data that will be fed to clf.decision_function or clf.predict_proba. y_true : array-like Gold standard target values for X. These must be class labels, not decision function values. **kwargs : dict Other parameters passed to the scorer. Refer to :func:`set_score_request` for more details. Returns ------- score : float Score function applied to prediction of estimator on X. zThere is an overlap between set kwargs of this scorer instance and passed metadata. Please pass them either as kwargs to `make_scorer` or metadata, but not both.rNr) rr rrrs_get_response_method_namerrr) rP method_callerrArrrCrr?y_predscoring_kwargss rFr[z_Scorer._scoreTs: .  )3D9L9L9N 0D`. :func:`~sklearn.metrics.get_scorer_names` can be used to retrieve the names of all available scorers. Parameters ---------- scoring : str, callable or None Scoring method as string. If callable it is returned as is. If None, returns None. Returns ------- scorer : callable The scorer. Notes ----- When passed a string, this function always returns a copy of the scorer object. Calling `get_scorer` twice for the same scorer results in two separate scorer objects. Examples -------- >>> import numpy as np >>> from sklearn.dummy import DummyClassifier >>> from sklearn.metrics import get_scorer >>> X = np.reshape([0, 1, -1, -0.5, 2], (-1, 1)) >>> y = np.array([0, 1, 1, 0, 1]) >>> classifier = DummyClassifier(strategy="constant", constant=0).fit(X, y) >>> accuracy = get_scorer("accuracy") >>> accuracy(classifier, X, y) 0.4 r&z]%r is not a valid scoring value. Use sklearn.metrics.get_scorer_names() to get valid options.)rYstrmax_error_scorerrr_SCORERSKeyError ValueError)rrbs rF get_scorerrstT'3 +%*x'89 M M (*12  s (AAc*eZdZdZdZdZdZdZy)_PassthroughScorercp||_t|jj} t j |j j|_||_y#t$rM t j |jj|_n#t$rYnwxYwY||_ywxYw)Nrx) _estimatorrr}rtrrrrTAttributeError_get_default_requests)rPrArequestss rFrRz_PassthroughScorer.__init__s#")@)@A !]]9+F+F+L+LMHN"*   !%y/N/N/P/V/V!W!  "*  s5.A B5)2BB5 B(%B5'B((B54B5c&|j|i|S)z!Method that wraps estimator.scorerU)rPrArBrCs rFrdz_PassthroughScorer.__call__sy///rHc4|jjdS)Nz.score)rr}rs rFrkz_PassthroughScorer.__repr__s//++,F33rHc,t|jS)a\Get requested data properties. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.3 Returns ------- routing : MetadataRouter A :class:`~utils.metadata_routing.MetadataRouter` encapsulating routing information. )rrrs rFrz'_PassthroughScorer.get_metadata_routings&d&<&<==rHc ts td|jD],\}}|jjj ||.|S)awSet requested parameters by the scorer. Please see :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.5 Parameters ---------- kwargs : dict Arguments should be of the form ``param_name=alias``, and `alias` can be one of ``{True, False, None, str}``. rr)rrrXrrTrrs rFrz$_PassthroughScorer.set_score_requests^ !E  #LLN OLE5  " " ( ( 4 45 4 N O rHN)rtrrrRrdrkrrrVrHrFrrs *04> rHrc  d|d}t|tttfrd} t|}t |t |k7rt |d|t |dkDratd|Ds2td|Drt |d |t |d ||Dcic]}|t|| }}|St |d |t|trwt|}td |Dst d|t |dk(rt d||jDcic]\}}|t|| }}}|St |#t$r}t ||d}~wwxYwcc}wcc}}w)a!Check the scoring parameter in cases when multiple metrics are allowed. In addition, multimetric scoring leverages a caching mechanism to not call the same estimator response method multiple times. Hence, the scorer is modified to only use a single response method given a list of response methods and the estimator. Parameters ---------- estimator : sklearn estimator instance The estimator for which the scoring will be applied. scoring : list, tuple or dict Strategy to evaluate the performance of the cross-validated model on the test set. The possibilities are: - a list or tuple of unique strings; - a callable returning a dictionary where they keys are the metric names and the values are the metric scores; - a dictionary with metric names as keys and callables a values. See :ref:`multimetric_grid_search` for an example. Returns ------- scorers_dict : dict A dict mapping each scorer name to its validated scorer. zscoring is invalid (got zh). Refer to the scoring glossary for details: https://scikit-learn.org/stable/glossary.html#term-scoringzFThe list/tuple elements must be unique strings of predefined scorers. Nz2 Duplicate elements were found in the given list. rc3<K|]}t|tywrMrYrrnrs rFrpz-_check_multimetric_scoring..:s8az!S)8c32K|]}t|ywrM)callablers rFrpz-_check_multimetric_scoring..;s1qx{1szi One or more of the elements were callables. Use a dict of score name mapped to the scorer callable. Got z4 Non-string types were found in the given list. Got rz Empty list was given. c3<K|]}t|tywrMrrs rFrpz-_check_multimetric_scoring..Os4!:a%4rzCNon-string types were found in the keys of the given dict. scoring=zAn empty dict was passed. ) rYlisttupler TypeErrorrrqallru check_scoringdictrX) rArerr_msg_genericerr_msgrrcrbrQkeys rF_check_multimetric_scoringrs> #7+.E E 'D%-. T  -w PWEK i@@G, N%y(?{KL L GT "7|4t44++2+7  t9>9'EF F '}} V y&9 9   N))U -W%1 , -, s# E(-FF ( F1 E==Fcd |jS#t$rt|jcYSwxYwrM)rtrrfuncr?s rFrr`s3?''' ?()=)=>>?s //>r predict_probadecision_functionrboolean)rr?greater_is_better)r?rc v|rdnd}|tjdtd}n|dk(rd}t||||S)a Make a scorer from a performance metric or loss function. A scorer is a wrapper around an arbitrary metric or loss function that is called with the signature `scorer(estimator, X, y_true, **kwargs)`. It is accepted in all scikit-learn estimators or functions allowing a `scoring` parameter. The parameter `response_method` allows to specify which method of the estimator should be used to feed the scoring/loss function. Read more in the :ref:`User Guide `. Parameters ---------- score_func : callable Score function (or loss function) with signature ``score_func(y, y_pred, **kwargs)``. response_method : {"predict_proba", "decision_function", "predict"} or list/tuple of such str, default=None Specifies the response method to use get prediction from an estimator (i.e. :term:`predict_proba`, :term:`decision_function` or :term:`predict`). Possible choices are: - if `str`, it corresponds to the name to the method to return; - if a list or tuple of `str`, it provides the method names in order of preference. The method returned corresponds to the first method in the list and which is implemented by `estimator`. - if `None`, it is equivalent to `"predict"`. .. versionadded:: 1.4 .. deprecated:: 1.6 None is equivalent to 'predict' and is deprecated. It will be removed in version 1.8. greater_is_better : bool, default=True Whether `score_func` is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the `score_func`. **kwargs : additional arguments Additional parameters to be passed to `score_func`. Returns ------- scorer : callable Callable object that returns a scalar score; greater is better. Examples -------- >>> from sklearn.metrics import fbeta_score, make_scorer >>> ftwo_scorer = make_scorer(fbeta_score, beta=2) >>> ftwo_scorer make_scorer(fbeta_score, response_method='predict', beta=2) >>> from sklearn.model_selection import GridSearchCV >>> from sklearn.svm import LinearSVC >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, ... scoring=ftwo_scorer) rzresponse_method=None is deprecated in version 1.6 and will be removed in version 1.8. Leave it to its default value to avoid this warning.rr)rr FutureWarningr)rr?rrCrs rF make_scorerrgsN^"1rD  S  $ I %# :tV_ ==rHF)rz`Scoring method max_error was renamed to neg_max_error in version 1.6 and will be removed in 1.8.c t||dS)Nrrrrs rFpositive_likelihood_ratior "66 21 55rHc t||dS)Nrrrs rFnegative_likelihood_ratiorrrH)rr)rr?rrovo)r? multi_classweighted)r?raverageovrexplained_variancer2 neg_max_errorr%neg_median_absolute_errorneg_mean_absolute_error"neg_mean_absolute_percentage_errorneg_mean_squared_errorneg_mean_squared_log_errorneg_root_mean_squared_errorneg_root_mean_squared_log_errorneg_mean_poisson_devianceneg_mean_gamma_deviancer accuracytop_k_accuracyroc_auc roc_auc_ovr roc_auc_ovoroc_auc_ovr_weightedroc_auc_ovo_weightedbalanced_accuracyaverage_precision neg_log_lossneg_brier_scorerneg_negative_likelihood_ratior6r<r9r7r=r:r5r;r8c<ttjS)aGet the names of all available scorers. These names can be passed to :func:`~sklearn.metrics.get_scorer` to retrieve the scorer object. Returns ------- list of str Names of all available scorers. Examples -------- >>> from sklearn.metrics import get_scorer_names >>> all_scorers = get_scorer_names() >>> type(all_scorers) >>> all_scorers[:3] ['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score'] >>> "roc_auc" in all_scorers True )sortedrrrVrHrFget_scorer_namesrcs, (--/ ""rH precisionrecallf1jaccardbinary)r)macromicrosamplesrz{0}_{1})rrfit)rAr allow_nonerK)r#rKct|tr t|St|ret |dd}t |drA|j dr0|j ds|j dstd|zt|St|ttttfrt||}t|| S|(t |d r t|S|rytd |zy) a* Determine scorer from user options. A TypeError will be thrown if the estimator cannot be scored. Parameters ---------- estimator : estimator object implementing 'fit' or None, default=None The object to use to fit the data. If `None`, then this function may error depending on `allow_none`. scoring : str, callable, list, tuple, set, or dict, default=None Scorer to use. If `scoring` represents a single score, one can use: - a single string (see :ref:`scoring_parameter`); - a callable (see :ref:`scoring_callable`) that returns a single value. If `scoring` represents multiple scores, one can use: - a list, tuple or set of unique strings; - a callable returning a dictionary where the keys are the metric names and the values are the metric scorers; - a dictionary with metric names as keys and callables a values. The callables need to have the signature `callable(estimator, X, y)`. If None, the provided estimator object's `score` method is used. allow_none : bool, default=False Whether to return None or raise an error if no `scoring` is specified and the estimator has no `score` method. raise_exc : bool, default=True Whether to raise an exception (if a subset of the scorers in multimetric scoring fails) or to return an error code. - If set to `True`, raises the failing scorer's exception. - If set to `False`, a formatted string of the exception details is passed as result of the failing scorer(s). This applies if `scoring` is list, tuple, set, or dict. Ignored if `scoring` is a str or a callable. .. versionadded:: 1.6 Returns ------- scoring : callable A scorer callable object / function with signature ``scorer(estimator, X, y)``. Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.metrics import check_scoring >>> from sklearn.tree import DecisionTreeClassifier >>> X, y = load_iris(return_X_y=True) >>> classifier = DecisionTreeClassifier(max_depth=2).fit(X, y) >>> scorer = check_scoring(classifier, scoring='accuracy') >>> scorer(classifier, X, y) 0.96... >>> from sklearn.metrics import make_scorer, accuracy_score, mean_squared_log_error >>> X, y = load_iris(return_X_y=True) >>> y *= -1 >>> clf = DecisionTreeClassifier().fit(X, y) >>> scoring = { ... "accuracy": make_scorer(accuracy_score), ... "mean_squared_log_error": make_scorer(mean_squared_log_error), ... } >>> scoring_call = check_scoring(estimator=clf, scoring=scoring, raise_exc=False) >>> scores = scoring_call(clf, X, y) >>> scores {'accuracy': 1.0, 'mean_squared_log_error': 'Traceback ...'} rN startswithzsklearn.metrics.zsklearn.metrics._scorerzsklearn.metrics.tests.zscoring value %r looks like it is a metric function rather than a scorer. A scorer should require an estimator as its first parameter. Please use `make_scorer` to convert a metric to a scorer.r)rQrKrTziIf no scoring is specified, the estimator passed should have a 'score' method. The estimator %r does not.)rYrrrgetattrhasattrr%rrrrrrrJrr)rArr#rKmodulerQs rFrrst'3'"",5 FL )!!"45%%&?@%%&>?") ) '""'D%d34,YH!'YGG 9g &%i0 0 DFOP  rHc |tjddg}nMtj||k(d}tj||k7d}tj||g}||||k\jtS)z;Threshold `y_score` and return the associated class labels.rr)nparray flatnonzeroastypeint)y_score thresholdclassesrmap_thresholded_score_to_label pos_label_idx neg_label_idxs rF!_threshold_scores_to_class_labelsr5s)+1a&)9&w)';>  &,%A%A%C"rHc 0|j}|||j||}i|j|}t|jt rHt jt j|t j||j} nt j|j} | D cgc]9} |j|j|t|| |j|fi|z;} } t j| | fScc} w)aEvaluate predicted target values for X relative to y_true. Parameters ---------- method_caller : callable Returns predictions given an estimator, method name, and other arguments, potentially caching results. estimator : object Trained estimator to use for scoring. X : {array-like, sparse matrix} of shape (n_samples, n_features) Test data that will be fed to estimator.predict. y_true : array-like of shape (n_samples,) Gold standard target values for X. **kwargs : dict Other parameters passed to the scorer. Refer to :func:`set_score_request` for more details. Returns ------- scores : ndarray of shape (thresholds,) The scores associated to each threshold. potential_thresholds : ndarray of shape (thresholds,) The potential thresholds used to compute the scores. r)rrsrrYr:rr*linspaceminmaxasarrayrrr5classes_r+) rPrrArrrCrr/rpotential_thresholdsthscore_thresholdss rFr[z_CurveScorer._scoreAs<'')  t,,a9 4DLL3F3 d&& 1#%;;w$2B2B$ $&::d.>.>#? +   JJd1R!3!3Y !      xx()+???  s<>D) rtrrrrR classmethodr@r[ __classcell__)r}s@rFr7r7s'2&  5@rHr7rV)NN)rrr collectionsr functoolsrinspectrnumbersr tracebackrnumpyr*baser utilsr utils._param_validationr r r rutils._responserutils.metadata_routingrrrrrrrrutils.validationrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4clusterr5r6r7r8r9r:r;r<r=rGrJrZrrrrrrrrrrexplained_variance_scorer r2_scorerneg_max_error_scorerrdeprecation_msgrneg_mean_squared_error_scorer!neg_mean_squared_log_error_scorerneg_mean_absolute_error_scorer)neg_mean_absolute_percentage_error_scorer neg_median_absolute_error_scorer"neg_root_mean_squared_error_scorer&neg_root_mean_squared_log_error_scorer neg_mean_poisson_deviance_scorerneg_mean_gamma_deviance_scorerd2_absolute_error_scoreraccuracy_scorerbalanced_accuracy_scorermatthews_corrcoef_scorerrr positive_likelihood_ratio_scorer$neg_negative_likelihood_ratio_scorertop_k_accuracy_scorerroc_auc_scoreraverage_precision_scorerroc_auc_ovo_scorerroc_auc_ovo_weighted_scorerroc_auc_ovr_scorerroc_auc_ovr_weighted_scorerneg_log_loss_scorerneg_brier_score_scorerbrier_score_loss_scoreradjusted_rand_scorer rand_scorerhomogeneity_scorercompleteness_scorerv_measure_scorermutual_info_scoreradjusted_mutual_info_scorernormalized_mutual_info_scorerfowlkes_mallows_scorerrrrrametricrformatqualified_namerrr5r7rVrHrFrsa $  UU2   68    _ _ DJ$JZ1Ok1OhC4(#'  4  4n@+@FVr?j    H I :yk* +  ([ #' $-M> M>b((@A  ! 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