-i'z |SSKrSSKrSSKrSSKJr SSKJr SSKrSSK J r SSK J r JrJr SSKJr SSKJrJrJr SSKJrJr SS KJrJr SS KJrJr SS KJ r SS K!J"r"J#r#J$r$ /S Qr%"SS\\ SS9r&"SS\\ SS9r'\"SS/S/\"\SSSS9/\"\SSSS9/S/S.SS9SSSS.Sj5r(Sr)S r*"S!S"\\ SS9r+g)#N) defaultdict)Integral) BaseEstimatorTransformerMixin _fit_context) column_or_1d)device get_namespacexpx)_encode_unique)Intervalvalidate_params)type_of_target unique_labels) min_max_axis) _num_samples check_arraycheck_is_fitted)LabelBinarizer LabelEncoderMultiLabelBinarizerlabel_binarizecD^\rSrSrSrSrSrSrSrU4Sjr Sr U=r $) raEncode target labels with value between 0 and n_classes-1. This transformer should be used to encode target values, *i.e.* `y`, and not the input `X`. Read more in the :ref:`User Guide `. .. versionadded:: 0.12 Attributes ---------- classes_ : ndarray of shape (n_classes,) Holds the label for each class. See Also -------- OrdinalEncoder : Encode categorical features using an ordinal encoding scheme. OneHotEncoder : Encode categorical features as a one-hot numeric array. Examples -------- `LabelEncoder` can be used to normalize labels. >>> from sklearn.preprocessing import LabelEncoder >>> le = LabelEncoder() >>> le.fit([1, 2, 2, 6]) LabelEncoder() >>> le.classes_ array([1, 2, 6]) >>> le.transform([1, 1, 2, 6]) array([0, 0, 1, 2]...) >>> le.inverse_transform([0, 0, 1, 2]) array([1, 1, 2, 6]) It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. >>> le = LabelEncoder() >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) LabelEncoder() >>> list(le.classes_) [np.str_('amsterdam'), np.str_('paris'), np.str_('tokyo')] >>> le.transform(["tokyo", "tokyo", "paris"]) array([2, 2, 1]...) >>> list(le.inverse_transform([2, 2, 1])) [np.str_('tokyo'), np.str_('tokyo'), np.str_('paris')] c:[USS9n[U5UlU$)zFit label encoder. Parameters ---------- y : array-like of shape (n_samples,) Target values. Returns ------- self : returns an instance of self. Fitted label encoder. Twarnr rclasses_selfys O/var/www/html/venv/lib/python3.13/site-packages/sklearn/preprocessing/_label.pyfitLabelEncoder.fitPs   &   c>[USS9n[USS9uUlnU$)zFit label encoder and return encoded labels. Parameters ---------- y : array-like of shape (n_samples,) Target values. Returns ------- y : array-like of shape (n_samples,) Encoded labels. Trreturn_inverser r"s r% fit_transformLabelEncoder.fit_transformas(  &"1T: qr(c[U5 [U5up#[XRRSS9n[ U5S:XaUR /5$[XRS9$)zTransform labels to normalized encoding. Parameters ---------- y : array-like of shape (n_samples,) Target values. Returns ------- y : array-like of shape (n_samples,) Labels as normalized encodings. T)dtyperr)uniques)rr r r!r/rasarrayr )r#r$xp_s r% transformLabelEncoder.transformrsW a  --"5"5D A ?a ::b> !q--00r(c [U5 [U5up#[USS9n[U5S:XaUR /5$[ R "UURURRS[U5S9US9nURS(a[S[U5-5eUR U5nURURUSS9$)zTransform labels back to original encoding. Parameters ---------- y : array-like of shape (n_samples,) Target values. Returns ------- y_original : ndarray of shape (n_samples,) Original encoding. Trr)r )r2z'y contains previously unseen labels: %saxis)rr r rr1r setdiff1daranger!shaper ValueErrorstrtake)r#r$r2r3diffs r%inverse_transformLabelEncoder.inverse_transforms a   & ?a ::b> !}} IIdmm))!,VAYI ?  ::a=FTRS S JJqMwwt}}aaw00r(cv>[TU]5nSUlSURlSUR lU$)NTF)super__sklearn_tags__array_api_support input_tags two_d_array target_tags one_d_labelsr#tags __class__s r%rDLabelEncoder.__sklearn_tags__s7w')!%&+#(,% r()r!) __name__ __module__ __qualname____firstlineno____doc__r&r,r4r@rD__static_attributes__ __classcell__rLs@r%rrs'/b""1,1<r(r)auto_wrap_output_keysc^\rSrSr%Sr\/\/S/S.r\\S'SSSS.S jr \ "S S 9S 5r S r Sr SSjrU4SjrSrU=r$)ra Binarize labels in a one-vs-all fashion. Several regression and binary classification algorithms are available in scikit-learn. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme. At learning time, this simply consists in learning one regressor or binary classifier per class. In doing so, one needs to convert multi-class labels to binary labels (belong or does not belong to the class). `LabelBinarizer` makes this process easy with the transform method. At prediction time, one assigns the class for which the corresponding model gave the greatest confidence. `LabelBinarizer` makes this easy with the :meth:`inverse_transform` method. Read more in the :ref:`User Guide `. Parameters ---------- neg_label : int, default=0 Value with which negative labels must be encoded. pos_label : int, default=1 Value with which positive labels must be encoded. sparse_output : bool, default=False True if the returned array from transform is desired to be in sparse CSR format. Attributes ---------- classes_ : ndarray of shape (n_classes,) Holds the label for each class. y_type_ : str Represents the type of the target data as evaluated by :func:`~sklearn.utils.multiclass.type_of_target`. Possible type are 'continuous', 'continuous-multioutput', 'binary', 'multiclass', 'multiclass-multioutput', 'multilabel-indicator', and 'unknown'. sparse_input_ : bool `True` if the input data to transform is given as a sparse matrix, `False` otherwise. See Also -------- label_binarize : Function to perform the transform operation of LabelBinarizer with fixed classes. OneHotEncoder : Encode categorical features using a one-hot aka one-of-K scheme. Examples -------- >>> from sklearn.preprocessing import LabelBinarizer >>> lb = LabelBinarizer() >>> lb.fit([1, 2, 6, 4, 2]) LabelBinarizer() >>> lb.classes_ array([1, 2, 4, 6]) >>> lb.transform([1, 6]) array([[1, 0, 0, 0], [0, 0, 0, 1]]) Binary targets transform to a column vector >>> lb = LabelBinarizer() >>> lb.fit_transform(['yes', 'no', 'no', 'yes']) array([[1], [0], [0], [1]]) Passing a 2D matrix for multilabel classification >>> import numpy as np >>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]])) LabelBinarizer() >>> lb.classes_ array([0, 1, 2]) >>> lb.transform([0, 1, 2, 1]) array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0]]) boolean neg_label pos_label sparse_output_parameter_constraintsrFc(XlX lX0lgNrZ)r#r[r\r]s r%__init__LabelBinarizer.__init__ s""*r(Tprefer_skip_nested_validationcURUR:a&[SURSURS35eUR(aEURS:XdURS:wa%[SURSUR35e[ USS9UlS UR ;a [S 5e[ U5S:Xa[S U-5e[R"U5Ul [U5Ul U$) aFit label binarizer. Parameters ---------- y : ndarray of shape (n_samples,) or (n_samples, n_classes) Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Returns ------- self : object Returns the instance itself. z neg_label=z& must be strictly less than pos_label=.rz`Sparse binarization is only supported with non zero pos_label and zero neg_label, got pos_label=z and neg_label=r$) input_name multioutput@Multioutput target data is not supported with label binarizationy has 0 samples: %r) r[r\r<r]ry_type_rspissparse sparse_input_rr!r"s r%r&LabelBinarizer.fits >>T^^ +T^^,-!^^,A/    4>>Q#6$..A:M!^^,ODNN;KM  &aC8 DLL (R  ?a 2Q67 7[[^%a(  r(cBURU5RU5$)aQFit label binarizer/transform multi-class labels to binary labels. The output of transform is sometimes referred to as the 1-of-K coding scheme. Parameters ---------- y : {ndarray, sparse matrix} of shape (n_samples,) or (n_samples, n_classes) Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL. Returns ------- Y : {ndarray, sparse matrix} of shape (n_samples, n_classes) Shape will be (n_samples, 1) for binary problems. Sparse matrix will be of CSR format. )r&r4r"s r%r,LabelBinarizer.fit_transform;s(xx{$$Q''r(c[U5 [U5RS5nU(a+URRS5(d [ S5e[ UUR URURURS9$)aKTransform multi-class labels to binary labels. The output of transform is sometimes referred to by some authors as the 1-of-K coding scheme. Parameters ---------- y : {array, sparse matrix} of shape (n_samples,) or (n_samples, n_classes) Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL. Returns ------- Y : {ndarray, sparse matrix} of shape (n_samples, n_classes) Shape will be (n_samples, 1) for binary problems. Sparse matrix will be of CSR format. multilabelz0The object was not fitted with multilabel input.)classesr\r[r]) rr startswithrlr<rr!r\r[r])r#r$y_is_multilabels r%r4LabelBinarizer.transformQsr( (+66|D 4<<#:#:<#H#HOP P MMnnnn,,   r(c[U5 UcURUR-S- nURS:Xa[ XR 5nO![ XRUR U5nUR(a[R"U5nU$[R"U5(aUR5nU$)aTransform binary labels back to multi-class labels. Parameters ---------- Y : {ndarray, sparse matrix} of shape (n_samples, n_classes) Target values. All sparse matrices are converted to CSR before inverse transformation. threshold : float, default=None Threshold used in the binary and multi-label cases. Use 0 when ``Y`` contains the output of :term:`decision_function` (classifier). Use 0.5 when ``Y`` contains the output of :term:`predict_proba`. If None, the threshold is assumed to be half way between neg_label and pos_label. Returns ------- y_original : {ndarray, sparse matrix} of shape (n_samples,) Target values. Sparse matrix will be of CSR format. Notes ----- In the case when the binary labels are fractional (probabilistic), :meth:`inverse_transform` chooses the class with the greatest value. Typically, this allows to use the output of a linear model's :term:`decision_function` method directly as the input of :meth:`inverse_transform`. g@ multiclass) rr\r[rl_inverse_binarize_multiclassr!_inverse_binarize_thresholdingrorm csr_matrixrntoarray)r#Y thresholdy_invs r%r@ LabelBinarizer.inverse_transformss@   $..8C?I <<< '0MMBE2<< E   MM%(E [[  MMOE r(ch>[TU]5nSURlSURlU$NFT)rCrDrFrGrHrIrJs r%rDLabelBinarizer.__sklearn_tags__/w')&+#(,% r()r!r[r\ror]rlra)rNrOrPrQrRrr^dict__annotations__rbrr&r,r4r@rDrSrTrUs@r%rrsmVrZZ#$D %&%+ 5&6&P(,  D1fr(r array-likez sparse matrixneither)closedrY)r$rur[r\r]Trdr_FrZc[U[5(d[USSSSS9nO[U5S:Xa[ SU-5eX#:a[ SR X#55eU(a&US:XdUS:wa[ S R X255eUS:HnU(aU*n[ U5nS U;a [ S 5eUS :Xa [ S 5e[R"U5(aURSO [U5n[U5n[R"U5nUS:XacUS:XaLU(a[R"US4[S9$[R"[U5S4[S9n X- n U $[U5S:aSn[R "U5n US:Xab[#US5(aURSO [US5n UR$U :wa$[ SR U['U555eUS;a[)U5n[R*"X5n X n [R,"X5n[R."S[R0"U 545n[R2"U5nUR5U5 [R"UX4Xx4S9n OiUS:XaU[R"U5n US:wa8[R2"U R65nUR5U5 UU lO[ SU-5eU(d@U R95n U R;[SS9n US:waX)U S:H'U(aSXU:H'O#U R6R;[SS9U l[R<"X:g5(a[R,"X5nU SS2U4n US:Xa+U(a U SS2S/4n U $U SS2S4R?S5n U $)anBinarize labels in a one-vs-all fashion. Several regression and binary classification algorithms are available in scikit-learn. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme. This function makes it possible to compute this transformation for a fixed set of class labels known ahead of time. Parameters ---------- y : array-like or sparse matrix Sequence of integer labels or multilabel data to encode. classes : array-like of shape (n_classes,) Uniquely holds the label for each class. neg_label : int, default=0 Value with which negative labels must be encoded. pos_label : int, default=1 Value with which positive labels must be encoded. sparse_output : bool, default=False, Set to true if output binary array is desired in CSR sparse format. Returns ------- Y : {ndarray, sparse matrix} of shape (n_samples, n_classes) Shape will be (n_samples, 1) for binary problems. Sparse matrix will be of CSR format. See Also -------- LabelBinarizer : Class used to wrap the functionality of label_binarize and allow for fitting to classes independently of the transform operation. Examples -------- >>> from sklearn.preprocessing import label_binarize >>> label_binarize([1, 6], classes=[1, 2, 4, 6]) array([[1, 0, 0, 0], [0, 0, 0, 1]]) The class ordering is preserved: >>> label_binarize([1, 6], classes=[1, 6, 4, 2]) array([[1, 0, 0, 0], [0, 1, 0, 0]]) Binary targets transform to a column vector >>> label_binarize(['yes', 'no', 'no', 'yes'], classes=['no', 'yes']) array([[1], [0], [0], [1]]) r$csrFN)rh accept_sparse ensure_2dr/rrkz7neg_label={0} must be strictly less than pos_label={1}.zuSparse binarization is only supported with non zero pos_label and zero neg_label, got pos_label={0} and neg_label={1}rirjunknownz$The type of target data is not knownbinaryr_r/rzmultilabel-indicatorr;z:classes {0} mismatch with the labels {1} found in the data)rrzr;z7%s target data is not supported with label binarization)copy)rr_) isinstancelistrrr<formatrrmrnr;lennpr1r}intzerossorthasattrsizerr isin searchsortedhstackcumsum empty_likefilldatar~astypeanyreshape)r$rur[r\r] pos_switchy_type n_samples n_classesr sorted_class y_n_classes y_in_classesy_seenindicesindptrrs r%rrsL a    #Ue4  ?a 2Q67 7 E L L   )q.IN vi+   aJJ A F N  ?@@ kk!nn #a&IG Ijj!G  >}}i^3??HHc!fa[4 \Q !F777#L ''$+Aw$7$7aggajS1Y <<; &LSS]1-  )) Owwq* //,7Aryy678}}W% ) MM41)9O P ) ) MM!  >==(D IIi AF E N    IIK HHSuH % >!a1fI  !A9n s/ vvg%&&//,8 ajM  !bT' A H!R%  )A Hr(c"[R"U5n[R"U5(GaUR 5nUR up#[R "U5n[US5Sn[R"UR5n[R"XV5n[R"XpR:H5nUSS:Xa+[R"U[UR5/5n[R"XRSS5n [R"UR S/5n XU n SU [R""US:H5S'[R "U5US:UR%5S:H-n U HMn UR URU URU S-nU[R&"XN5SX'MO X$UR)UR+SS9SS9$)zuInverse label binarization transformation for multiclass. Multiclass uses the maximal score instead of a threshold. r_rrNr7clip)mode)rr1rmrntocsrr;r:rr?rrepeat flatnonzerorappendrrrwhereravelr9r>argmax)r$rur n_outputsoutputsrow_maxrow_nnzy_data_repeated_maxy_i_all_argmaxindex_first_argmax y_ind_ext y_i_argmaxsamplesiinds r%r{r{bs jj!G {{1~~ GGI ww ))I&q!$Q'''!((# ii9(;vv(EF 2;! YY~AFF }EN __^XXcr]KIIaii!- .@AB 01 288GqL)!,-))I&!  18L'MNA))AHHQK!((1q5/:C#BLL$>?BJM""||AHH!H,6|::r(c2US:XaGURS:Xa7URSS:a$[SRUR55eUS:wa'URS[ U5:wa [S5e[ R "U5n[R"U5(aUS:a\URS;aUR5n[ R"URU:[S9Ul UR5 OF[ R"UR5U:[S9nO[ R"X:[S9nUS:Xa[R"U5(aUR5nURS:XaURSS:Xa X S S 2S4$[ U5S:Xa#[ R"US[ U55$X R!5$US :XaU$[S RU55e) z=Inverse label binarization transformation using thresholding.rrr_z'output_type='binary', but y.shape = {0}zAThe number of class is not equal to the number of dimension of y.r)rcscrNrz{0} format is not supported)ndimr;r<rrrr1rmrnrarrayrreliminate_zerosr~rr)r$ output_typerurs r%r|r|sh166Q;1771:>BII!''RSSh1771:W#= O  jj!G {{1~~ q=xx~-GGIXXaffy0> A 66Q;1771:?QT7# #7|q yySV44wwy)) . .6==kJKKr(c^\rSrSr%SrSS/S/S.r\\S'SSS.S jr\ "S S 9S 5r \ "S S 9S 5r Sr Sr SrSrU4SjrSrU=r$)ria<Transform between iterable of iterables and a multilabel format. Although a list of sets or tuples is a very intuitive format for multilabel data, it is unwieldy to process. This transformer converts between this intuitive format and the supported multilabel format: a (samples x classes) binary matrix indicating the presence of a class label. Parameters ---------- classes : array-like of shape (n_classes,), default=None Indicates an ordering for the class labels. All entries should be unique (cannot contain duplicate classes). sparse_output : bool, default=False Set to True if output binary array is desired in CSR sparse format. Attributes ---------- classes_ : ndarray of shape (n_classes,) A copy of the `classes` parameter when provided. Otherwise it corresponds to the sorted set of classes found when fitting. See Also -------- OneHotEncoder : Encode categorical features using a one-hot aka one-of-K scheme. Examples -------- >>> from sklearn.preprocessing import MultiLabelBinarizer >>> mlb = MultiLabelBinarizer() >>> mlb.fit_transform([(1, 2), (3,)]) array([[1, 1, 0], [0, 0, 1]]) >>> mlb.classes_ array([1, 2, 3]) >>> mlb.fit_transform([{'sci-fi', 'thriller'}, {'comedy'}]) array([[0, 1, 1], [1, 0, 0]]) >>> list(mlb.classes_) ['comedy', 'sci-fi', 'thriller'] A common mistake is to pass in a list, which leads to the following issue: >>> mlb = MultiLabelBinarizer() >>> mlb.fit(['sci-fi', 'thriller', 'comedy']) MultiLabelBinarizer() >>> mlb.classes_ array(['-', 'c', 'd', 'e', 'f', 'h', 'i', 'l', 'm', 'o', 'r', 's', 't', 'y'], dtype=object) To correct this, the list of labels should be passed in as: >>> mlb = MultiLabelBinarizer() >>> mlb.fit([['sci-fi', 'thriller', 'comedy']]) MultiLabelBinarizer() >>> mlb.classes_ array(['comedy', 'sci-fi', 'thriller'], dtype=object) rNrYrur]r^FcXlX lgrar)r#rur]s r%rbMultiLabelBinarizer.__init__s  *r(TrdcSUlURc2[[[R R U555nOL[[UR55[UR5:a [S5eURn[SU55(a[O[n[R"[U5US9UlX RSS&U$)a,Fit the label sets binarizer, storing :term:`classes_`. Parameters ---------- y : iterable of iterables A set of labels (any orderable and hashable object) for each sample. If the `classes` parameter is set, `y` will not be iterated. Returns ------- self : object Fitted estimator. NztThe classes argument contains duplicate classes. Remove these duplicates before passing them to MultiLabelBinarizer.c3B# UHn[U[5v M g7frarr.0cs r% *MultiLabelBinarizer.fit..s?w!:a--wr) _cached_dictrusortedset itertoolschain from_iterablerr<allrobjectremptyr!)r#r$rur/s r%r&MultiLabelBinarizer.fits ! << S!>!>q!ABCG T\\" #c$,,&7 7/  llG?w???VWU; " a r(cpURb URU5RU5$SUl[ [ 5nUR UlURX5n[X"RS9n[SU55(a[ O[n[R"[U5US9nXBSS&[R "USS9uUln[R$"XcR&UR&R(S9UlUR*(dUR-5nU$)aFit the label sets binarizer and transform the given label sets. Parameters ---------- y : iterable of iterables A set of labels (any orderable and hashable object) for each sample. If the `classes` parameter is set, `y` will not be iterated. Returns ------- y_indicator : {ndarray, sparse matrix} of shape (n_samples, n_classes) A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]` is in `y[i]`, and 0 otherwise. Sparse matrix will be of CSR format. Nkeyc3B# UHn[U[5v M g7frarrs r%r4MultiLabelBinarizer.fit_transform..Bs;s!:a--srrTr*)rur&r4rrr__len__default_factory _transformrgetrrrrruniquer!r1rr/r]r~)r#r$ class_mappingyttmpr/inverses r%r,!MultiLabelBinarizer.fit_transform"s$ << #88A;((+ + $C( (5(=(= % __Q .](9(9:;s;;;S7 a!#=!N wZZ 32::;K;KL !!B r(c[U5 UR5nURX5nUR(dUR 5nU$)aTransform the given label sets. Parameters ---------- y : iterable of iterables A set of labels (any orderable and hashable object) for each sample. If the `classes` parameter is set, `y` will not be iterated. Returns ------- y_indicator : array or CSR matrix, shape (n_samples, n_classes) A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]` is in `y[i]`, and 0 otherwise. )r _build_cacherr]r~)r#r$class_to_indexrs r%r4MultiLabelBinarizer.transformNsA **, __Q /!!B r(c URc@[[UR[ [ UR5555UlUR$ra)rrzipr!ranger)r#s r%r MultiLabelBinarizer._build_cachehs@    $ $Sc$-->P8Q%R SD    r(c |[R"S5n[R"SS/5n[5nUHUn[5nUHnURX(5 M UR U5 UR [ U55 MW U(a1[R"SR[U[S955 [R"[ U5[S9n [R "XU4[ U5S- [ U54S9$![a URU5 Mf=f)aTransforms the label sets with a given mapping. Parameters ---------- y : iterable of iterables A set of labels (any orderable and hashable object) for each sample. If the `classes` parameter is set, `y` will not be iterated. class_mapping : Mapping Maps from label to column index in label indicator matrix. Returns ------- y_indicator : sparse matrix of shape (n_samples, n_classes) Label indicator matrix. Will be of CSR format. rrz%unknown class(es) {0} will be ignoredrrr_r)rraddKeyErrorextendrrwarningsrrrr=ronesrrmr}) r#r$rrrrlabelsindexlabelrs r%rMultiLabelBinarizer._transformns$++c"S1#&%FEE'IIm23 NN5 ! MM#g, '  MM7>>vgSV?WX wws7|3/}} F #CK!OS=O+P   'KK&'sDD;:D;c [U5 URS[UR5:wa;[ SR [UR5URS55e[ R"U5(aUR5n[UR5S:wa;[[R"URSS/55S:a [ S5e[URSSURSS5VVs/sH5up#[URRUR X#55PM7 snn$[R"USS/5n[U5S:a[ SR U55eUVs/sH'n[URR#U55PM) sn$s snnfs snf)aPTransform the given indicator matrix into label sets. Parameters ---------- yt : {ndarray, sparse matrix} of shape (n_samples, n_classes) A matrix containing only 1s ands 0s. Returns ------- y_original : list of tuples The set of labels for each sample such that `y[i]` consists of `classes_[j]` for each `yt[i, j] == 1`. r_z/Expected indicator for {0} classes, but got {1}rz+Expected only 0s and 1s in label indicator.Nrz8Expected only 0s and 1s in label indicator. Also got {0})rr;rr!r<rrmrnrrrr9rrtupler>rcompress)r#rstartend unexpected indicatorss r%r@%MultiLabelBinarizer.inverse_transforms  88A;#dmm, ,AHH &   ;;r??B277|q Sbgg1v)F%G!%K !NOO#&biinbiim"D"DJEdmm((E)>?@"D  b1a&1J:" NUU" QSSPR*E$--00<=PRS STs [TU]5nSURlSURlU$r)rCrDrFrGrH two_d_labelsrJs r%rD$MultiLabelBinarizer.__sklearn_tags__rr()rrur!r])rNrOrPrQrRr^rrrbrr&r,r4rrr@rDrSrTrUs@r%rrs<~!$'#$D #'e+56@5)6)V4! & P'TRr(r),rrr collectionsrnumbersrnumpyr scipy.sparsesparsermbaserrrutilsr utils._array_apir r r utils._encoder rutils._param_validationrrutils.multiclassrrutils.sparsefuncsrutils.validationrrr__all__rrrr{r|rr(r%r$s #@@ 99,?<,II M#]$M`|%}D|~O , >xtIFGxtIFG# #' -.%h  h V(;V)LXJ*MQUJr(