:i]>SrSSKrSSKJr SSKJr SSKJrJr SSK J r J r SSK r SSKJr SSKJr SS KJr SS KJrJr SS KJr SS KJr S SKJr SrSr"SS5rSr S$Sjr!Sr"S%Sjr#Sr$Sr%Sr&Sr'Sr(Sr)Sr*Sr+Sr,S r-\(\%\'\&\$\)S!.r.S"r/S#r0g)&zUtilities for input validationN) OrderedDict)wraps) Parameter signature)IntegralReal)issparse)clone)NearestNeighbors) check_array column_or_1d)type_of_target) _num_samples) _is_pandas_df) over-samplingunder-samplingclean-samplingensemblebypass)binary multiclassmultilabel-indicatorc0\rSrSrSrSrSrSrSrSr g) ArraysTransformer zAA class to convert sampler output arrays to their original types.c\URU5UlURU5UlgN) _gets_propsx_propsy_propsselfXys ]/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/imblearn/utils/_validation.py__init__ArraysTransformer.__init__#s&''* ''* cURXR5nURX R5nURSR5S:Xa2URSR5S;aURUlX4$)Ntype dataframe>seriesr,)_transfrom_oner r!lowerindexr"s r& transformArraysTransformer.transform'su   << 0   << 0 <<  % % '; 64<< < %',<- ggAGt r)c0nURRUS'[USS5US'[USS5US'[USS5US'U$)Nr+columnsnamedtypes) __class____name__getattr)r#arraypropss r&rArraysTransformer._gets_props2sV00f "5)T:ivt4f !%48h r)c USR5nUS:XaUR5nU$US:XadSSKn[U5(a'URR R XSS9nOUR XSS9nURUS5nU$US :XaSSKnURXSUS S 9nU$UnU$![a URHnnXFR5R5(dM*XFRS:XdM>USUS :XdMLURS /[XF5-5XF'Mp URUS5nU$f=f)Nr+listr,rr4)r4r6zdatetime64[ns]ztimedelta64[ns]NaTr-r5)dtyper5)r/tolistpandasr DataFramesparse from_spmatrixastype TypeErrorr4isnullallr@ to_timedeltalenSeries)r#r:r;type_retpdcols r&r. ArraysTransformer._transfrom_one:sef ##% F?,,.C@ ?k ! ll))77YGW7Xll5 2BlC 2jjx1, h  ))ExuV})MC C + 2 ;;C)--//HNN.>>!(OC04EE#%??E7S]3J#K 'jjx1 + 2s$B<<# UHn[TU5v M g7fr)hasattr).0attr estimators r& '_is_neighbors_object..qsI4HDwy$''4Hs)rI)r_neighbors_attributess` r&_is_neighbors_objectrc`s# )*<= I4HI IIr)cX[U[5(a [X-S9$[U5$)aCheck the objects is consistent to be a k nearest neighbors. Several methods in `imblearn` relies on k nearest neighbors. These objects can be passed at initialisation as an integer or as an object that has KNeighborsMixin-like attributes. This utility will create or clone said object, ensuring it is KNeighbors-like. Parameters ---------- nn_name : str The name associated to the object to raise an error if needed. nn_object : int or KNeighborsMixin The object to be checked. additional_neighbor : int, default=0 Sometimes, some algorithm need an additional neighbors. Returns ------- nn_object : KNeighborsMixin The k-NN object. ) n_neighbors) isinstancerr r )nn_name nn_objectadditional_neighbors r&check_neighbors_objectrjts*0)X&&I,KLL  r)cX[R"USS9up[[X55$)NT) return_counts)npuniquedictzip)r%rncountss r&_count_class_samplerrs$YYq5NF F# $$r)c[U5nUS:XaF[R"URSS9S:5(a [ S5eUR SS9nO [ U5nU(aXS:H4$U$)aLCheck the target types to be conform to the current samplers. The current samplers should be compatible with ``'binary'``, ``'multilabel-indicator'`` and ``'multiclass'`` targets only. Parameters ---------- y : ndarray The array containing the target. indicate_one_vs_all : bool, default=False Either to indicate if the targets are encoded in a one-vs-all fashion. Returns ------- y : ndarray The returned target. is_one_vs_all : bool, optional Indicate if the target was originally encoded in a one-vs-all fashion. Only returned if ``indicate_multilabel=True``. rr)axiszImbalanced-learn currently supports binary, multiclass and binarized encoded multiclasss targets. Multilabel and multioutput targets are not supported.)rrmanysum ValueErrorargmaxr )r%indicate_one_vs_alltype_ys r&check_target_typer{sx.A F '' 66!%%Q%-!# $ $9  HH!H  O4GA// 0NQNr)cX[U5nUS:XaB[UR55nUR5VVs0sH upEXCU- _M nnnU$US:XdUS:Xa;[ UR55nUR 5Vs0sHoDU_M nnU$[ es snnfs snf)z1Returns sampling target by targeting all classes.rrr)rrmaxvaluesitemsminkeysNotImplementedError)r% sampling_type target_statsn_sample_majoritykeyvaluesampling_strategyn_sample_minoritys r&_sampling_strategy_allrs&q)L' 3 3 56?K?Q?Q?S ?S|CU* *?S    * *m?O.O 3 3 56?K?P?P?RS?R"33?RS "! Ts B! B'cUS:Xa [S5eUS:XdUS:Xa`[U5n[X"RS9n[ UR 55nUR 5Vs0sH nXS:XdM XT_M nnU$[es snf)z=Returns sampling target by targeting the majority class only.rz@'sampling_strategy'='majority' cannot be used with over-sampler.rrr)rwrrr}getrr~rr)r%rrclass_majorityrrrs r&_sampling_strategy_majorityrs' N   * *m?O.O*1- \/?/?@ 3 3 56$((* *$ #C "*   "!  s ' B4Bc[U5nUS:Xa\[UR55n[X"RS9nUR 5VVs0sHupVXT:wdM XSU- _M nnnU$US:XdUS:XaU[ UR55n[X"RS9nUR 5Vs0sH nXT:wdM XX_M nnU$[es snnfs snf)zFReturns sampling target by targeting all classes but not the majority.rrrr)rrr}r~rrrrr) r%rrrrrrrrs r&_sampling_strategy_not_majorityr'q)L' 3 3 56\/?/?@!- 2 2 4 4 $ +CU* * 4   * *m?O.O 3 3 56\/?/?@$((* *$ #C "*   "!   C C8 CCc[U5nUS:Xa\[UR55n[X"RS9nUR 5VVs0sHupVXT:wdM XSU- _M nnnU$US:XdUS:XaU[UR55n[X"RS9nUR 5Vs0sH nXT:wdM XX_M nnU$[es snnfs snf)zFReturns sampling target by targeting all classes but not the minority.rrrr)rrr}r~rrrrr) r%rrrclass_minorityrrrrs r&_sampling_strategy_not_minorityrrrc"[U5nUS:Xa\[UR55n[X"RS9nUR 5VVs0sHupVXT:XdM XSU- _M nnnU$US:XdUS:Xa [ S5e[es snnf)z=Returns sampling target by targeting the minority class only.rrrrzS'sampling_strategy'='minority' cannot be used with under-sampler and clean-sampler.)rrr}r~rrrrwr)r%rrrrrrrs r&_sampling_strategy_minorityrs&q)L' 3 3 56\/?/?@!- 2 2 4 4 $ +CU* * 4    * *m?O.O 0  "! s  B  B cTUS:Xa [X5$US:XdUS:Xa [X5$g)zSReturns sampling target auto for over-sampling and not-minority for under-sampling.rrrN)rr)r%rs r&_sampling_strategy_autor-s9'.q@@ * *m?O.O.q@@/Pr)c[U5n[UR55[UR55- n[U5S:a[ SUS35e[ SUR 555(a[ SU35e0nUS:Xal[UR 55 [X3RS9 UR5H*upgXsU:a[ SX6S US 35eXsU- XV'M, U$US :Xa;UR5H%upgXsU:a[ S X6S US 35eXuU'M' U$US :Xa [ S5e[e)zOReturns sampling target by converting the dictionary depending of the sampling.rThe - target class is/are not present in the data.c3*# UH oS:v M g7f)rNrW)r] n_sampless r&r`*_sampling_strategy_dict..Ds E*DYq=*DszfThe number of samples in a class cannot be negative.'sampling_strategy' contains some negative value: rrzWith over-sampling methods, the number of samples in a class should be greater or equal to the original number of samples. Originally, there is z samples and z samples are asked.rzWith under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is rz'sampling_strategy' as a dict for cleaning methods is not supported. Please give a list of the classes to be targeted by the sampling.) rrsetrrKrwrur~r}rrr)rr%rr!set_diff_sampling_strategy_targetsampling_strategy_ class_samplers r&_sampling_strategy_dictr6s'q)L(+,=,B,B,D(EI)% ,-1456# $   E*;*B*B*D EEEAAR@S U  ' L   !" L../'8'>'>'@ #L 55 -.:-G,HI##,+-@ B09 ;U/U  ,(A< ) * *'8'>'>'@ #L 55 -.:-G,HI##,+-@ B09| ,(A&  * * (  "!r)cUS:wa [S5e[U5n[U5[UR55- n[ U5S:a[SUS35eUVs0sHoU[ UR 55_M sn$s snf)zWWith cleaning methods, sampling_strategy can be a list to target the class of interest.rzQ'sampling_strategy' cannot be a list for samplers which are not cleaning methods.rrr)rwrrrrrKrr~)rr%rrrrs r&_sampling_strategy_listrns(( .  'q)L(+,=(>B)% ,-1456# $  FW EV\c,--/00EV  s$#B c H[U5nUS:wa [S5e[U5nUS:Xa[UR 55n[XDR S9nUR 5VVs0sHupxXv:wdM U[XP-U- 5_M n nn[U R 5V s/sHoS:*PM sn 5(a [S5eU $US:Xa[UR 55n [XDR S9n UR 5VVs0sHupxX|:wdM U[X- 5_M n nn[U R 5V V s/sH upXU :PM sn n 5(a [S5eU $[S 5es snnfs sn fs snnfs sn n f) znTake a proportion of the majority (over-sampling) or minority (under-sampling) class in binary classification.rzg"sampling_strategy" can be a float only when the type of target is binary. For multi-class, use a dict.rrrzThe specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.rzThe specified ratio required to generate new sample in the majority class while trying to remove samples. Please increase the ratio.zD'clean-sampling' methods do let the user specify the sampling ratio.) rrwrrr}r~rrintrur)rr%rrzrrrrrrrrrtargets r&_sampling_strategy_floatrsA F  @  'q)L' 3 3 56\/?/?@!- 2 2 4 4 $ DC&:UBC C 4  0B0I0I0KL0K9Q0KL M M  N< / * * 3 3 56\/?/?@!- 2 2 4 4 $ rrrcallable)rr%rkwargsrs r&check_sampling_strategyrs$pM)/ ?!?) -   yy|A99Q<$$%^ 5  ..  #S)) $8$=$=$? ?!!5 6g>O=PQ   '(9:1LRRT U   %t , , *+<OUUW X   %t , , *+<OUUW X   %t , ,  !%6%:,,=+>iI  ():}MSSU   # $ $.q;F; '(:}MSSU    %r))minoritymajorityz not minorityz not majorityrIautocd^^^^[T5m/m/mTRR5HgupUR[R :XaTR U5 M6UR[R:XdMVTR U5 Mi [T5UUUU4Sj5nU$)aDecorator for methods that issues warnings for positional arguments Using the keyword-only argument syntax in pep 3102, arguments after the * will issue a warning when passed as a positional argument. Parameters ---------- f : function function to check arguments on. c>[U5[T5- nUS:aY[T SUX*S5VVs/sH up4USU3PM nnn[R"SSR U5S3[ 5 UR [T RU5VVs0sHupdXd_M snn5 T"S0UD6$s snnfs snnf)Nr=zPass z, z` as keyword args. From version 0.9 passing these as positional arguments will result in an errorrW)rKrpwarningswarnjoin FutureWarningupdate parameters) argsr extra_argsr5argargs_msgkall_argsf kwonly_argssigs r&inner_f+_deprecate_positional_args..inner_ffsYX. >"%[*%=tKL?Q!R!RID&#!R  MM (+,-%&    C,EF,E&!qv,EFG{6{Gs B:C ) rrrkindrPOSITIONAL_OR_KEYWORDappend KEYWORD_ONLYr)rr5paramrrrrs` @@@r&_deprecate_positional_argsrQs A,CKH~~++-  ::88 8 OOD ! ZZ911 1   t $ .  1X" Nr)c[U5nUS:a[SUS35e[U5(aU$[USSS/SS9$) z+Check X and do not check it if a dataframe.rzFound array with z, sample(s) while a minimum of 1 is required.NcsrcscF)r@ accept_sparseforce_all_finite)rrwrr )r$rs r&_check_Xr{s\QI1} {+   Q  eU^e r))r)F)1rUr collectionsr functoolsrinspectrrnumbersrrnumpyrm scipy.sparser sklearn.baser sklearn.neighborsr sklearn.utilsr r sklearn.utils.multiclassrsklearn.utils.validationrfixesrr TARGET_KINDrrcrjrrr{rrrrrrrrrrrrrrWr)r&rs$ #("!.331  ? ==@J(<% #OL"*44,A5p20fJ \,+33 ! # 'T r)