rtj'"`dZddlZddlZddlmZddlmZddlmZhdZ d dZ dZ d Z d Z dS) z+Utilities to discover scikit-learn objects.N) import_module) itemgetter)Path>setuptestsconftest externals experimentalestimator_checksc ddlmm}m}m}m}ddlm}dg}tttj j }|t5tj|gdD]\}} }| d} t!d | Dsd | vr9t#| } t%j| t$j} d | D} ||  d d d n #1swxYwYt-|}fd |D} fd| D} |t/|t0s|g}nt1|}g}||||d}|D]?\}||vr6|||fd| D@|} |rt7d|dt9t-| t;dS)a1Get a list of all estimators from `sklearn`. This function crawls the module and gets all classes that inherit from BaseEstimator. Classes that are defined in test-modules are not included. Parameters ---------- type_filter : {"classifier", "regressor", "cluster", "transformer"} or list of such str, default=None Which kind of estimators should be returned. If None, no filter is applied and all estimators are returned. Possible values are 'classifier', 'regressor', 'cluster' and 'transformer' to get estimators only of these specific types, or a list of these to get the estimators that fit at least one of the types. Returns ------- estimators : list of tuples List of (name, class), where ``name`` is the class name as string and ``class`` is the actual type of the class. Examples -------- >>> from sklearn.utils.discovery import all_estimators >>> estimators = all_estimators() >>> type(estimators) >>> type(estimators[0]) >>> estimators[:2] [('ARDRegression', ), ('AdaBoostClassifier', )] >>> classifiers = all_estimators(type_filter="classifier") >>> classifiers[:2] [('AdaBoostClassifier', ), ('BaggingClassifier', )] >>> regressors = all_estimators(type_filter="regressor") >>> regressors[:2] [('ARDRegression', ), ('AdaBoostRegressor', )] >>> both = all_estimators(type_filter=["classifier", "regressor"]) >>> both[:2] [('ARDRegression', ), ('AdaBoostClassifier', )] r) BaseEstimatorClassifierMixin ClusterMixinRegressorMixinTransformerMixinignore_warningscVt|dsdSt|jsdSdS)N__abstractmethods__FT)hasattrlenr)cs U/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/utils/discovery.py is_abstractz#all_estimators..is_abstractSs7011 51()) 5tcategorysklearn.pathprefix.c3(K|] }|tvVdSN_MODULE_TO_IGNORE.0parts r z!all_estimators..b(GG$D--GGGGGGr._cFg|]\}}|d||fS_) startswith)r(nameest_clss r z"all_estimators..hsB$1D'DOOTWDXDXwrNcXg|]&}t|dr|ddk$|'S)rr  issubclass)r(rr s rr3z"all_estimators..psJ qt] + +12!0G0G 0G0G0Grc6g|]}|d|Sr5)r(rrs rr3z"all_estimators..vs,AAA{{1Q4/@/@A!AAAr) classifier regressor transformerclusterc@g|]}t|d|Sr9r6)r(estmixins rr3z"all_estimators..s,LLLS*SVU2K2KLSLLLrz_Parameter type_filter must be 'classifier', 'regressor', 'transformer', 'cluster' or None, got key) sklearn.baser rrrrsklearn.utils._testingrstrr__file__parent FutureWarningpkgutil walk_packagessplitanyrinspect getmembersisclassextendset isinstancelistitemsremove ValueErrorsortedr) type_filterrrrrr all_classesrootr/ module_name module_partsmoduleclasses estimatorsfiltered_estimatorsfiltersr1r rrAs @@@rall_estimatorsrcs$h766666K tH~~$+ , ,D - 0 0 0((!(!6TF:!V!V!V ( ( A{A&,,S11LGG,GGGGG ;&&";//F(AAG5<G   w ' ' ' ' (((((((((((((((( k""KJ BAAAZAAAJ+t,, ,&-KK{++K )'+#    #==??  KD%{""""4(((#**LLLLJLLL)  % %%%  #j//z!}} 5 5 55sB!DD D cRddlm}g}tttjj}|t 5tj|gdD]\}}}| d}td|Dsd|vr9t|}tj |tj}d |D}|| d d d n #1swxYwYt!t#|t%d S) aGet a list of all displays from `sklearn`. Returns ------- displays : list of tuples List of (name, class), where ``name`` is the display class name as string and ``class`` is the actual type of the class. Examples -------- >>> from sklearn.utils.discovery import all_displays >>> displays = all_displays() >>> displays[0] ('CalibrationDisplay', ) rrrrrr"c3(K|] }|tvVdSr$r%r's rr*zall_displays..r+rr,cpg|]3\}}|d|d/||f4S)r/Display)r0endswith)r(r1 display_classs rr3z all_displays..sY'D-s++15 i0H0H}%rNrB)rErrFrrGrHrIrJrKrLrMrrNrOrPrQrXrRr)rrZr[r/r\r]r^r_s r all_displaysrjs"766666K tH~~$+ , ,D - 0 0 0((!(!6TF:!V!V!V ( ( A{A&,,S11LGG,GGGGG ;&&";//F(AAG+2G   w ' ' ' ' ((((((((((((((((" #k"" 1 6 6 66sB!C33C7:C7ctj|sdS|jdrdS|j}|dr|drdSdS)NFr/rr T)rN isfunction__name__r0 __module__rh)itemmods r_is_checked_functionrqsp  d # #u }$$u /C >>* % %6H)I)Iu 4rcHddlm}g}tttjj}|t 5tj|gdD]\}}}| d}td|Dsd|vr9t|}tj |t}d |D}|| d d d n #1swxYwYt!t#|t%d S) aGet a list of all functions from `sklearn`. Returns ------- functions : list of tuples List of (name, function), where ``name`` is the function name as string and ``function`` is the actual function. Examples -------- >>> from sklearn.utils.discovery import all_functions >>> functions = all_functions() >>> name, function = functions[0] >>> name 'accuracy_score' rrrrrr"c3(K|] }|tvVdSr$r%r's rr*z all_functions..r+rr,cPg|]#\}}|d|j|f$Sr.)r0rm)r(r1funcs rr3z!all_functions..sED$s++%rNrB)rErrFrrGrHrIrJrKrLrMrrNrOrqrQrXrRr)r all_functionsr[r/r\r]r^ functionss rrvrvs$766666M tH~~$+ , ,D - 0 0 0,,!(!6TF:!V!V!V , , A{A&,,S11LGG,GGGGG ;&&";//F*63GHHI"+I   + + + + ,,,,,,,,,,,,,,,,* #m$$*Q-- 8 8 88sBC..C25C2r$)__doc__rNrJ importlibroperatorrpathlibrr&rcrjrqrvr:rrr|s11 ######@6@6@6@6F(7(7(7V   -9-9-9-9-9r