-iOSrSSKrSSKJr SSKJr SSKrSSKJ r SSK J r SSK J r S S KJrJr S S KJrJr SS jrSS jr\\\S.rSrSrSrSrSSjrSSjrSSjrSrg)zAUtilities to handle multiclass/multioutput target in classifiers.N)Sequence)chain)issparse) get_namespace)VisibleDeprecationWarning) attach_unique cached_unique)_assert_all_finite check_arrayc[XS9up[US5(dU(a[URU5US9$[ U5$)Nxp __array__)rhasattrr asarraysetyris_array_api_compliants K/var/www/html/venv/lib/python3.13/site-packages/sklearn/utils/multiclass.py_unique_multiclassrs=!.q!8Bq+"8RZZ]r221v cj[XS9upUR[US/SQS9RS5$)Nrrcsrcsccoo) input_name accept_sparser )raranger shape)rr_s r_unique_indicatorr%s8 ! #EB 99A#5JKQQRST r)binary multiclassmultilabel-indicatorc &^^[USS06n[U6umn[U5S:Xa [S5e[ SU55nUSS1:XaS1n[U5S:a[S U-5eUR 5nUS :Xa*[[ S U555S:a [S 5e[ RUS 5mT(d[S[U5-5eU(a8TRUVs/sH nT"UTS9PM sn5nTRU5$[ [R"UU4SjU555n[[ SU555S:a [S5eTR[U55$s snf)a)Extract an ordered array of unique labels. We don't allow: - mix of multilabel and multiclass (single label) targets - mix of label indicator matrix and anything else, because there are no explicit labels) - mix of label indicator matrices of different sizes - mix of string and integer labels At the moment, we also don't allow "multiclass-multioutput" input type. Parameters ---------- *ys : array-likes Label values. Returns ------- out : ndarray of shape (n_unique_labels,) An ordered array of unique labels. Examples -------- >>> from sklearn.utils.multiclass import unique_labels >>> unique_labels([3, 5, 5, 5, 7, 7]) array([3, 5, 7]) >>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4]) array([1, 2, 3, 4]) >>> unique_labels([1, 2, 10], [5, 11]) array([ 1, 2, 5, 10, 11]) return_tupleTrzNo argument has been passed.c38# UHn[U5v M g7fN)type_of_target).0xs r unique_labels..Os1b>!$$bsr&r'r z'Mix type of y not allowed, got types %sr(c3T# UHn[U/SQS9RSv M g7f)r)r!r N)r r#)r.rs rr0r1\s(VXQR A-BCII!LVXs&(zCMulti-label binary indicator input with different numbers of labelsNzUnknown label type: %src3@># UHnST"UTS95v M g7f)c3$# UHov M g7fr,)r.is rr0*unique_labels...qsA(@1Q(@srNr5)r.r_unique_labelsrs rr0r1qs!N2aAqR(@AA2sc3B# UHn[U[5v M g7fr,) isinstancestr)r.labels rr0r1ts=9%z%%%9sz,Mix of label input types (string and number))r rlen ValueErrorrpop_FN_UNIQUE_LABELSgetreprconcat unique_valuesr from_iterablersorted) ysrys_types label_typer unique_ys ys_labelsr8rs @@r unique_labelsrL)s@  . .B!.!3B 2w!|7881b11HHl++ > 8}qBXMNNJ ,,  VX    Q  '**:tBGHH ::fY' ((Es7Fc [U5upURURS5=(aP [UR UR UR XR 5UR5U:H55$)N real floating)risdtypedtypeboolallastypeint64rs r_is_integral_floatrUzs_!.q!1B ::agg / D ryy"))Axx0177;q@A5rc [U5up[US5(d[U[5(dU(aV[ SSSSSSS9n[ R "5 [ R"S[5 [U4SS0UD6nSSS5 [US 5(a#URS :XaURS S :dg[!U5(aUR"S ;aUR%5nUR'UR(5n[+UR(5S:H=(dc UR,S :H=(d UR,S :H=(a SU;=(a+ UR.R0S;=(d [3U5$[5XS9nURSS:=(a. UR7UR.S5=(d [3U5$![[4a=n[U5RS 5(ae[U4S[0UD6nSnAGNSnAff=f!,(df  GN=f)a!Check if ``y`` is in a multilabel format. Parameters ---------- y : ndarray of shape (n_samples,) Target values. Returns ------- out : bool Return ``True``, if ``y`` is in a multilabel format, else ``False``. Examples -------- >>> import numpy as np >>> from sklearn.utils.multiclass import is_multilabel >>> is_multilabel([0, 1, 0, 1]) False >>> is_multilabel([[1], [0, 2], []]) False >>> is_multilabel(np.array([[1, 0], [0, 0]])) True >>> is_multilabel(np.array([[1], [0], [0]])) False >>> is_multilabel(np.array([[1, 0, 0]])) True rTFrr!allow_ndensure_all_finite ensure_2densure_min_samplesensure_min_featureserrorrPNComplex data not supportedr#rr )doklilbiur)rQzsigned integerzunsigned integer)rrr:rdictwarningscatch_warnings simplefilterrr r>r; startswithobjectndimr#rformattocsrrDdatar=sizerPkindrUr rO)rrrcheck_y_kwargselabelss r is_multilabelrrs8"/q!1Bq+*Q"9"9=S# !   $ $ &  ! !'+D E C@@@' Aw  AFFaKAGGAJN{{ 88~ % A!!!&&)166{a [[A  G6;;!#3"F!v+ F&D*B  C ' &s0H3;G##H032H+%H3+H00H33 IcB[USS9nUS;a[SUS35eg)a!Ensure that target y is of a non-regression type. Only the following target types (as defined in type_of_target) are allowed: 'binary', 'multiclass', 'multiclass-multioutput', 'multilabel-indicator', 'multilabel-sequences' Parameters ---------- y : array-like Target values. rr )r&r'zmulticlass-multioutputr(zmultilabel-sequenceszUnknown label type: zy. Maybe you are trying to fit a classifier, which expects discrete classes on a regression target with continuous values.N)r-r>)ry_types rcheck_classification_targetsrvsBA# .F "6(+8 8  rc T^^^[T5up4UUU4Sjn[T[5=(d [T5=(d [ TS5=(a [T[ 5(+=(d UnU(d[ ST-5eTRRS;nU(a [ S5e[T5(ag[SSSSS S S 9n[R"5 [R"S [5 [T5(d[T4S S 0UD6mS S S 5 [T5(a TS /S S 24OTS n [U [$5(a ['S5e[ U S5(d5[U [5(a [U [ 5(d [ S5eTR*S;aU"5$[-TR.5(dTR*S:XagU"5$[T5(d=TR0[":Xa)[TR2S [ 5(dU"5$TR*S:XaTR.SS:aSn OSn UR5TR0S5(aY[T5(a TR6OTn UR9XR;U [<5:g5(a[?U TS9 SU -$[W 5(a U R6n [AT5n TR.S S:a]TR.S U R.S s=:a[CSTR.S -5:aO O[RD"S[FSS9 U R.S S:dTR*S:Xa[IU 5S:aSU -$g![[ 4a=n [ U 5R!S5(ae[T4S ["0UD6mS n A GNS n A ff=f!,(df  GN=f![(a GNvf=f)a Determine the type of data indicated by the target. Note that this type is the most specific type that can be inferred. For example: * ``binary`` is more specific but compatible with ``multiclass``. * ``multiclass`` of integers is more specific but compatible with ``continuous``. * ``multilabel-indicator`` is more specific but compatible with ``multiclass-multioutput``. Parameters ---------- y : {array-like, sparse matrix} Target values. If a sparse matrix, `y` is expected to be a CSR/CSC matrix. input_name : str, default="" The data name used to construct the error message. .. versionadded:: 1.1.0 raise_unknown : bool, default=False If `True`, raise an error when the type of target returned by :func:`~sklearn.utils.multiclass.type_of_target` is `"unknown"`. .. versionadded:: 1.6 Returns ------- target_type : str One of: * 'continuous': `y` is an array-like of floats that are not all integers, and is 1d or a column vector. * 'continuous-multioutput': `y` is a 2d array of floats that are not all integers, and both dimensions are of size > 1. * 'binary': `y` contains <= 2 discrete values and is 1d or a column vector. * 'multiclass': `y` contains more than two discrete values, is not a sequence of sequences, and is 1d or a column vector. * 'multiclass-multioutput': `y` is a 2d array that contains more than two discrete values, is not a sequence of sequences, and both dimensions are of size > 1. * 'multilabel-indicator': `y` is a label indicator matrix, an array of two dimensions with at least two columns, and at most 2 unique values. * 'unknown': `y` is array-like but none of the above, such as a 3d array, sequence of sequences, or an array of non-sequence objects. Examples -------- >>> from sklearn.utils.multiclass import type_of_target >>> import numpy as np >>> type_of_target([0.1, 0.6]) 'continuous' >>> type_of_target([1, -1, -1, 1]) 'binary' >>> type_of_target(['a', 'b', 'a']) 'binary' >>> type_of_target([1.0, 2.0]) 'binary' >>> type_of_target([1, 0, 2]) 'multiclass' >>> type_of_target([1.0, 0.0, 3.0]) 'multiclass' >>> type_of_target(['a', 'b', 'c']) 'multiclass' >>> type_of_target(np.array([[1, 2], [3, 1]])) 'multiclass-multioutput' >>> type_of_target([[1, 2]]) 'multilabel-indicator' >>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]])) 'continuous-multioutput' >>> type_of_target(np.array([[0, 1], [1, 1]])) 'multilabel-indicator' cN>T(aT(aTOSn[SUST<35eg)zTDepending on the value of raise_unknown, either raise an error or return 'unknown'. rlzUnknown label type for z: unknown)r>)inputr raise_unknownrs r_raise_or_return(type_of_target.._raise_or_return3s- ",J&E6ugRuEF Frrz:Expected array-like (array or non-string sequence), got %r) SparseSeries SparseArrayz1y cannot be class 'SparseSeries' or 'SparseArray'r(TFrrWr]rPNr^zkSupport for labels represented as bytes is not supported. Convert the labels to a string or integer format.zYou appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead - the MultiLabelBinarizer transformer can convert to this format.)r rr r&rz -multioutputrNrt continuousg?zThe number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.) stacklevelr')%rr:rrrr;r> __class____name__rrrcrdrerfrr rgrhbytes TypeError IndexErrorriminr#rPflatrOrlanyrSintr r roundwarn UserWarningr=)rr r{rrr|valid sparse_pandasrorpfirst_row_or_valsuffixrlclassess``` rr-r-sZ"/q!1B Ax JHQK J71k3J #1c" "    H1 L  KK((,KKMLMMQ%  N  "g'@A{{ C@@@ # (0 1aS!V91 & . .<  (+66+X66/55;  vvV!! qww<< 66Q;!! A;;177f,Zq 35O5O!! vv{qwwqzA~ zz!''?++!!qvv! 66$))D#.. / / t ;&( ( !!+00AGwwqzB1771: a(8R5qwwqzAQ;RR  &   }}Q!! 4D0E0If$$].z: Cq6$$%ABB BB>B  C # "D    sC',PN7+BP7P2O?9P?PP P P'&P'c[USS5cUc [S5eUbm[USS5bN[R"UR[ U55(d[SU<SUR<35eg[ U5Ulgg)aPrivate helper function for factorizing common classes param logic. Estimators that implement the ``partial_fit`` API need to be provided with the list of possible classes at the first call to partial_fit. Subsequent calls to partial_fit should check that ``classes`` is still consistent with a previous value of ``clf.classes_`` when provided. This function returns True if it detects that this was the first call to ``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also set on ``clf``. classes_Nz8classes must be passed on the first call to partial_fit.z `classes=z7` is not the same as on last call to partial_fit, was: TF)getattrr>np array_equalrrL)clfrs r_check_partial_fit_first_callrssJ%-'/STT   3 D ) 5>>#,, g0FGG 18#,,HH  )1CL rc0/n/n/nURupVUb[R"U5n[U5(GaUR 5n[R "UR 5n[U5GHnURUR UUR US-n Ub2Xn [R"U5[R"U 5- n OSn URSXx- n [R"URUR UUR US-SS9up[R"XS9nSU ;aXS:H==U - ss'SU ;aEXxURS:a0[R"U SS5n [R"USU 5nURU 5 URU RS5 URXR5- 5 GM O[U5Hn[R"USS2U4SS9upURU 5 URU RS5 [R"XS9nURXR5- 5 M X#U4$)a>Compute class priors from multioutput-multiclass target data. Parameters ---------- y : {array-like, sparse matrix} of size (n_samples, n_outputs) The labels for each example. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- classes : list of size n_outputs of ndarray of size (n_classes,) List of classes for each column. n_classes : list of int of size n_outputs Number of classes in each column. class_prior : list of size n_outputs of ndarray of size (n_classes,) Class distribution of each column. Nr rT)return_inverse)weights)r#rrrtocscdiffindptrrangeindicessumuniquerlbincountinsertappend)r sample_weightr n_classes class_prior n_samples n_outputsy_nnzk col_nonzeronz_samp_weightzeros_samp_weight_sum classes_ky_k class_prior_ks rclass_distributionrs1,GIK77I  =1 {{ GGI!y!A))AHHQK!((1q5/BK(!.!;(*}(=~@V(V%!%() UX(=%YYqxx{QXXa!e_5dNIKKDMI~1n-1FF- !eh&;IIiA6 " -rssG $!,317!$-N)bE P 6JZ FG-T*+r