rtj ddlZddlZddlZddlmZddlmZddlZddl m Z ddl m Z mZmZddlmZmZddlmZmZmZmZmZmZmZmZmZddlmZmZddl m!Z!m"Z"dd l#m$Z$m%Z%dd l&m'Z'dd l(m)Z)m*Z*m+Z+gd Z,Gd dee dZ-Gddee dZ.e"ddgdge!edddge!edddgdgdddddddZ/d"dZ0d"dZ1Gd d!ee dZ2dS)#N) defaultdict)Integral) BaseEstimatorTransformerMixin _fit_context)_align_api_if_sparse column_or_1d) _find_matching_floating_dtype_is_numpy_namespace_isindevice get_namespaceget_namespace_and_deviceindexing_dtypemove_toxpx)_encode_unique)Intervalvalidate_params)type_of_target unique_labels) min_max_axis) _num_samples check_arraycheck_is_fitted)LabelBinarizer LabelEncoderMultiLabelBinarizerlabel_binarizec:eZdZdZdZdZdZdZfdZxZ S)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')] cPt|d}t||_|S)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 Z/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/preprocessing/_label.pyfitzLabelEncoder.fitZs(  & & &   cZt|d}t|d\|_}|S)aFit 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. Tr#return_inverser%r's r* fit_transformzLabelEncoder.fit_transformks4  & & &"1T::: qr,ct|t|\}}t||jjd}t |dkr|gSt||jS)aTransform 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)dtyper$r)uniques)rrr r&r2rasarrayr)r(r)xp_s r* transformzLabelEncoder.transform|sr a  A $-"5D A A A ??a  ::b>> !q$-0000r,c t|t|\}}t|d}t|dkr|gSt j|||jj dt||}|j drtdt|z||}| |j|dS)aTransform 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. Tr#rr r5z'y contains previously unseen labels: %saxis)rrr rr4r setdiff1daranger&shaper ValueErrorstrtake)r(r)r5r6diffs r*inverse_transformzLabelEncoder.inverse_transforms a  A  & & & ??a  ::b>> !} IIdm)!,VAYYI ? ?    :a= TFTRSS S JJqMMwwt}aaw000r,ct}d|_d|j_d|j_|S)NTF)super__sklearn_tags__array_api_support input_tags two_d_array target_tags one_d_labelsr(tags __class__s r*rGzLabelEncoder.__sklearn_tags__s:ww''))!%&+#(,% r,) __name__ __module__ __qualname____doc__r+r0r7rDrG __classcell__rOs@r*rr(s//b""111,111<r,r)auto_wrap_output_keysceZdZUdZegegdgdZeed<dddddZe d d Z d Z d Z ddZ fdZxZS)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_constraintsrFc0||_||_||_dSNrY)r(rZr[r\s r*__init__zLabelBinarizer.__init__s""*r,Tprefer_skip_nested_validationcb|j|jkr td|jd|jd|jr5|jdks |jdkrtd|jd|jt |\}}|r.|jr't |std|jdt|d |_d |jvrtd t|dkrtd |ztj ||_ t||_|S)aaFit 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=>`sparse_output=True` is not supported for array API namespace <. Use `sparse_output=False` to return a dense array instead.r)) input_name multioutput@Multioutput target data is not supported with label binarizationy has 0 samples: %r)rZr[r@r\rr rPry_type_rspissparse sparse_input_rr&)r(r)r5 is_array_apis r*r+zLabelBinarizer.fits >T^ + +/T^//!^///    4>Q#6#6$.A:M:MM!^MM<@NMM  )++L  D. 7J27N7N M[MMM  &aC888 DL ( (R  ??a  2Q677 7[^^%a((  r,cR|||S)aFit 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+r7r's r*r0zLabelBinarizer.fit_transformNs"(xx{{$$Q'''r,ct|t|\}}|r.|jr't|st d|jdt |d}|r)|jdst dt||j |j |j |jS)aTransform 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. rfrg multilabelz0The object was not fitted with multilabel input.)classesr[rZr\) rrr\r r@rPr startswithrlr r&r[rZ)r(r)r5rpy_is_multilabels r*r7zLabelBinarizer.transformds( (++L  D. 7J27N7N M[MMM  )++66|DD  Q4<#:#:<#H#H QOPP P Mnn,     r,Nct|t|\}}|r.|jr't|st d|jd||j|jzdz }|jdkrt||j |}nt||j|j ||}|jr"ttj|}n(tj|r|}|S)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`. zY`LabelBinarizer` was fitted on a sparse matrix, and therefore cannot inverse transform a z array back to a sparse matrix.Ng@ multiclassr:)rrror r@rPr[rZrl_inverse_binarize_multiclassr&_inverse_binarize_thresholdingrrm csr_arrayrntoarray)r(Y thresholdr5rpy_invs r*rDz LabelBinarizer.inverse_transforms#@ (++L  D. 7J27N7N T'){TTT   $.8C?I << ' '0DMbIIIEE24< bE   $(e)<)<==EE [   $MMOOE r,cxt}d|j_d|j_|SNFT)rFrGrIrJrKrLrMs r*rGzLabelBinarizer.__sklearn_tags__2ww''))&+#(,% r,r`)rPrQrRrSrr]dict__annotations__rarr+r0r7rDrGrTrUs@r*rrsVVrZZ#$$D %&%+++++ \555//65/b(((,) ) ) V9999vr,r array-likez sparse matrixneither)closedrX)r)rtrZr[r\Trbr^FrYc  t|tst|dddd}n%t|dkrt d|z||kr#t d|||r/|dks|dkr#t d |||dk}|r| }t |}d |vrt d |d krt d t|\}}} |r)|r't|st d|j d | || }n2#ttf$r} t d|j d| d} ~ wwxYwt|dr |j dnt|} |j d} t|d} | r#||jdr|j}nt#|}| r8||jdr|||jd}|dkrZ| dkrL|r*t't)j| dft,S|| df|}||z }|S| dkrd}||}|dkrht|dr |j dnt|d}| |kr0t d|t3||dvr3t5|}t7||| }||}|||}|||}|| dg| ||d!f}|||}t)jtA|tBd"#tA|tBd"#tA|tBd"#f| | f$}|s)| |"| }n|dkr|r=t)j|}|dkr"||j#|}||_#nbt)j$|r|"}| || d%&}|dkr |||dk<nt d'|z|s3|dkr |||dk<|r d|||k<|||d}n&|j#t,d|_#|%||kr"|||}|dd|f}|dkr0|r|ddd(gf}n |&|ddd(fd)}t'|S)*aBinarize 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_2dr2rrkz7neg_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 knownz?`sparse_output=True` is not supported for array API 'namespace z='. Use `sparse_output=False` to return a dense array instead.r9z>`classes` contains unsupported dtype for array API namespace 'z'.r?r2signed integerintegral)copybinaryr^r2rxmultilabel-indicatorz:classes {0} mismatch with the labels {1} found in the data)rrxr:r;cpu)r5r r?T)r rz7%s target data is not supported with label binarization)rr^)' isinstancelistrrr@formatrrr rPr4 TypeErrorhasattrr?lenisdtyper2rastyperrmr{intzerossortrr r searchsortedconcatcumulative_sum full_likernpr|datarnanyreshape)r)rtrZr[r\ pos_switchy_typer5rpdevice_e n_samples n_classes y_has_dtype int_dtype_r} sorted_class y_n_classes y_in_classesy_seenindicesindptrrs r*r r sL a  8  #Ue4    ??a  2Q677 7I E L L9      )q..INN vi++    aJJ A  F N   ?@@@ 8 ; ;B g  .A".E.E  I+ I I I   **WW*55  "        &a11= s1vvI a I!W%%K(rzz!'+;<<(W #B'' :rzz!':66:))GQW5)99  >> +BL)Qs,S,S,STTTHHi^:H>>Y !^^!F777##L '''$+Aw$7$7FagajjS1YY  # #LSS]1--  ))) OOQB/// <//,77yyz::  A3w //!!,Q!77    ||GY// LE222Bu5552e444  i(     8 199;;w 77A ) ) )  & QAA~~||AFI66{1~~ IIKK 1W4 88AA~~%!q&  E N     0 >>!Aa1fI  " !Aa9n  IIa%I 0 0s// vvg%&&//,88 aaajM   .!!!bT' AA 1QQQU8W--A  " ""s+EE2E--E2ctj|rtj|}|}|j\}}tj|}t|dd}tj|j }tj ||}tj ||j k} |ddkr(tj | t|j g} tj| |j dd} tj |jdg} | | | } d| tj|dkd<tj||dk|dkz} | D]N}|j|j ||j |dz}|tj||d| |<O|| St)||\}}}|||}||d}||d|jddz }||S)z}Inverse label binarization transformation for multiclass. Multiclass uses the maximal score instead of a threshold. r^rrNr:r9r;)rmrnrr4tocsrr?r>rrCrrepeat flatnonzerorappendrrrwhereravelr=rargmaxclip)r)rtr5r n_outputsoutputsrow_maxrow_nnzy_data_repeated_maxy_i_all_argmaxindex_first_argmax y_ind_ext y_i_argmaxsamplesiindr6rrs r*ryrys  {1~~' *W%% GGII w 9)I&&q!$$Q''!(## i99(;qv(EFF 2;!  Y~AF }EEN _^QXcrc]KKIai!-- ~.@AB 01 28GqL))!,-)I&&!  18L'MN C CA)AHQK!(1q5/9:C#BL#$>$>?BJqMMz""1!;;;Aw**WW*55))AA)&&'''1gmA&6&:;;wr,cX|dkrC|jdkr8|jddkr'td|jt ||\}}}|||}|dkr+|jd|jdkrtdt ||}t|d r#||j d r|j }nt|}tj |r|dkr[|jd vr| }tj|j|kt" |_|na|||k|| }n2||||| |k|| }|dkrtj |r|}|jdkr#|jddkr||dddfS|jddkr)||dt+|S|||dS|dkr|Std|)z=Inverse label binarization transformation using thresholding.rr^z'output_type='binary', but y.shape = {0}r:r9rzAThe number of class is not equal to the number of dimension of y.r2r)rcscr)r2r N)rrz{0} format is not supported)ndimr?r@rrr4r rrr2rrmrnrrarrayrreliminate_zerosr|rrr) r) output_typertr~r5r6rdtype_rs r*rzrzsh16Q;;171:>>BII!'RRSSS-aB777NB7jjj11Gh171:q1A#A#A O   +1 4 4 4Fq'(rzz!'3CDD(W #B''  {1~~  q==x~--GGIIXafy0<<>  A 6Q;;171:??1QQQT7# #}Q1$$yySVV444rzz!U3344 . . .6==kJJKKKr,ceZdZUdZddgdgdZeed<ddddZed d Z ed d Z d Z dZ dZ dZfdZxZS)ra?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. Read more in the :ref:`User Guide `. 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) rNrXrtr\r]Fc"||_||_dSr`r)r(rtr\s r*razMultiLabelBinarizer.__init__hs *r,Trbcd|_|j:tttj|}nMtt|jt|jkrtd|j}td|Drtnt}tj t|||_||jdd<|S)aFit 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.c3@K|]}t|tVdSr`rr.0cs r* z*MultiLabelBinarizer.fit..s,??!:a--??????r,r) _cached_dictrtsortedset itertoolschain from_iterablerr@allrobjectremptyr&)r(r)rtr2s r*r+zMultiLabelBinarizer.fitls ! < S!>!>q!A!ABBCCGG T\"" # #c$,&7&7 7 7/  lG??w?????KVWU;;; " aaa r,c~|j(|||Sd|_t t }|j|_|||}t||j }td|Drt nt}tjt||}||dd<tj|d\|_}tj||j|jj|_|js|}|S)aMFit 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. Nkeyc3@K|]}t|tVdSr`rrs r*rz4MultiLabelBinarizer.fit_transform..s,;;!:a--;;;;;;r,rTr.)rtr+r7rrr__len__default_factory _transformrgetrrrrruniquer&r4rr2r\r|)r(r) class_mappingyttmpr2inverses r*r0z!MultiLabelBinarizer.fit_transforms$ < #88A;;((++ + $C(( (5(= % __Q . .] (9:::;;s;;;;;GS777  aaa!#=!N!N!N wZ 32:;KLLL ! B r,ct||}|||}|js|}|S)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*r7zMultiLabelBinarizer.transformsS **,, __Q / /! B r,c |jFtt|jt t |j|_|jSr`)rrzipr&ranger)r(s r*rz MultiLabelBinarizer._build_cachesB   $ $Sc$->P>P8Q8Q%R%R S SD   r,c tjd}tjddg}t}|D]}t}|D]C} |||#t$r||Y@wxYw|||t ||r;tjd t|ttj t |t} tt!j| ||ft |dz t |fS)a/Transforms 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)rraddKeyErrorextendrrwarningsr$rrrAronesrrrmr{) r(r)rrrrlabelsindexlabelrs r*rzMultiLabelBinarizer._transformsu$+c""S1#&&%% ( (FEEE ' ''IImE23333'''KK&&&&&' NN5 ! ! ! MM#g,, ' ' ' '   M7>>vgSV?W?W?WXX   ws7||3///# Lw'F a]ASAS/T      sA,,B Bctjdtjkr@t dtjjdt jrtj dkrz9MultiLabelBinarizer.inverse_transform..sPE3dm((E#I)>??@@r,Nrz8Expected only 0s and 1s in label indicator. Also got {0}c^g|])}tj|*Sr)rr&compress)r indicatorsr(s r*r z9MultiLabelBinarizer.inverse_transform..*s1SSS*E$-00<<==SSSr,)rr?rr&r@rrmrnrrrr=rr)r(r unexpecteds`` r*rDz%MultiLabelBinarizer.inverse_transforms  8A;#dm,, , ,AHH &&   ;r?? TB27||q  Sbg1v)F)F%G%G!%K%K !NOOO"%binbim"D"D  b1a&11J:"" NUU" TSSSPRSSS Sr,cxt}d|j_d|j_|Sr)rFrGrIrJrK two_d_labelsrMs r*rGz$MultiLabelBinarizer.__sklearn_tags__,rr,)rPrQrRrSr]rrrarr+r0r7rrrDrGrTrUs@r*rr"s'>>B!$'#$$D #'e+++++\55565@\555))65)V4!!! ( ( ( T'T'T'TRr,rr`)3rrr collectionsrnumbersrnumpyr scipy.sparsesparserm sklearn.baserrr sklearn.utilsrr sklearn.utils._array_apir r r r rrrrrsklearn.utils._encoderrsklearn.utils._param_validationrrsklearn.utils.multiclassrrsklearn.utils.sparsefuncsrsklearn.utils.validationrrr__all__rrr ryrzrrr,r*rs% ######FFFFFFFFFF<<<<<<<<                      32222222EEEEEEEEBBBBBBBB222222OOOOOOOOOO   MMMMM#]$MMMM`VVVVV%}DVVVVrO , >hxtIFFFGhxtIFFFG# #'   -.%^#^#^#^#  ^#B, , , , ^4L4L4L4LnNNNNN*MQUNNNNNNr,