rtj#SdZddlZddlmZddlmZddlZddlm Z ddl m Z m Z ddl mZmZddlmZdd lmZmZmZdd Zdd Zeeed Zdd dZdZdZdZddZddZddZdZ dS)zAUtilities to handle multiclass/multioutput target in classifiers.N)Sequence)chain)issparse)_is_numpy_namespace get_namespace) attach_unique cached_unique)VisibleDeprecationWarning)_assert_all_finite _num_samples check_arrayct||\}}t|ds|r$t|||St |S)Nxp __array__)rhasattrr asarraysetyris_array_api_compliants V/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/utils/multiclass.py_unique_multiclassrs\!.qR!8!8!8Bq+"8RZZ]]r22221vv ct||\}}|t|dgdjdS)Nrrcsrcsccoo) input_name accept_sparse)raranger shape)rr_s r_unique_indicatorr&sP ! # # #EB 99A#5J5J5JKKKQRST  r)binary multiclassmultilabel-indicator)ys_typesc$t|ddi}t|\}t|dkrtd|$t d|D}|ddhkrdh}t|d krtd |z|}|d kr9tt d |Dd krtd t |dstdt|z|rFts7 fd|D} |St tj fd|D}tt d|Dd kr9ddfd|Dzdz}td|t!|S)aCExtract an ordered array of unique labels. We don't allow: - mix of multilabel and multiclass (single label) targets - mix of label indicator matrix and anything else (because there are no explicit labels) - mix of label indicator matrices of different sizes - mix of string and integer labels At the moment, we also don't allow "multiclass-multioutput" input type. Parameters ---------- *ys : array-likes Label values. ys_types : set, default=None Set of target types of `ys` (as determined by `type_of_target`), with `{"binary", "multiclass"}` being amended to `{"multiclass"}`. Returns ------- out : ndarray of shape (n_unique_labels,) An ordered array of unique labels. Examples -------- >>> from sklearn.utils.multiclass import unique_labels >>> unique_labels([3, 5, 5, 5, 7, 7]) array([3, 5, 7]) >>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4]) array([1, 2, 3, 4]) >>> unique_labels([1, 2, 10], [5, 11]) array([ 1, 2, 5, 10, 11]) return_tupleTrzNo argument has been passed.Nc34K|]}t|VdSN)type_of_target).0xs r z unique_labels..Ss*55Q~a((555555rr'r(r"z'Mix type of y not allowed, got types %sr)c3RK|]"}t|gdjdV#dS)r)r!r"N)r r$)r0rs rr2z unique_labels..bsOQR A-B-B-BCCCI!LrzCMulti-label binary indicator input with different numbers of labelszUnknown label type: %sc*g|]}|S)rr0r_unique_labelsrs r z!unique_labels..ss(DDDA~~aB777DDDrc3FK|]}d|DVdS)c3K|]}|VdSr.r5)r0is rr2z*unique_labels...ws"AA1QAAAAAArrNr5r6s rr2z unique_labels..wsANNaAAqR(@(@(@AAANNNNNNrc3@K|]}t|tVdSr.) isinstancestr)r0labels rr2z unique_labels..zs,==%z%%%======rzGot z and c<g|]}|Sr5) unique_values)r0rrs rr8z!unique_labels..|s*"H"H"Hb&6&6q&9&9#;"H"H"Hr.z.Mix of label input types (string and number); )rrlen ValueErrorrpop_FN_UNIQUE_LABELSgetreprrconcatrAr from_iterablejoinrsorted) r*ysr label_type unique_ys ys_labels msg_detailsr7rs @@r unique_labelsrR)suH  . . .B!.!3B 2ww!||788855"55555 ,/ / /$~H 8}}qBXMNNNJ ,,,  VX       Q   '**:t<1DHH<===+&9"&=&=+IIDDDDDDDDEE  *** NNNNN2NNNNNI 3==9=== = =>>BB W\\"H"H"H"HR"H"H"HII IC O W+WWXXX ::fY'' ( ((rc  t|\}}||jdoWt|||||j|j|kS)N real floating)risdtypedtypeboolallastypeint64rs r_is_integral_floatr[sr!.q!1!1B ::ag / / D ryy"))Arx0017;;q@AA55rct|\}}t|dst|ts|rt dddddd}t j5t jdt t|fddi|}nU#ttf$rA}t| d rt|fdti|}Yd}~nd}~wwxYwdddn #1swxYwYt|d r|jd kr|jd d ksdSt!|r|jd vr|}||j}t+|jdkp6|jd ks|jd ko d|vo|jjdvpt3|St5||}|jddko)||jdpt3|S)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_featureserrorrVNComplex data not supportedr$r")doklilbiur)rWzsigned integerzunsigned integer)rrr=rdictwarningscatch_warnings simplefilterr r rDr> startswithobjectndimr$rformattocsrrAdatarCsizerVkindr[r rU)rrrcheck_y_kwargselabelss r is_multilabelrys8"/q!1!1Bq+C*Q"9"9C=SC# !     $ & & C C  !'+D E E E C@@@@@-z: C C Cq66$$%ABB BBB>BB  C C C C C C C C C C C C C C C C Aw  AFaKKAGAJNNu{{ 8~ % % A!!!&))16{{a [A  G6;!#3"F!v+ F&D*B C/C 7CC/C  C//C36C3c(t|d}|dvrtd|dd|vrat|}|dkrNt|jdt d |zkr"t jd td d Sd Sd Sd S)aAEnsure 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.r(rg?zThe number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.re) stacklevelN) r/rDr r r$roundrkwarn UserWarning)ry_type n_sampless rcheck_classification_targetsrsA# . . .F  86 8 8 8   v OO r>>mA..4Q7%i:P:PPP M*         >PPrFclt\}}fd}ttstst dott  p|}|st dzjjdv}|rt dtrdStddddd d }tj 5tj d ttsf tfd d i|nU#tt f$rA} t | drtfd t"i|Yd } ~ nd } ~ wwxYwd d d n #1swxYwY tr d gd d fnd } t| t$rt'dt | ds9t| tr$t| t st dn#t($rYnwxYwjdvr |St-jsjdkrdS|Sts:jt"kr*tjd t s |Sjdkrjddkrd} nd} |jdr{trjn} || |j} || || jkrt?| d| zSt| r| j} tAjd dksjdkrtC| dkrd| zSdS)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' cBrrnd}td|ddS)zdDepending on the value of raise_unknown, either raise an error or return 'unknown'. rszUnknown label type for z: unknown)rD)inputr raise_unknownrs r_raise_or_returnz(type_of_target.._raise_or_returnHs?  ",8JJ&EEuEEEEFF F9rrz:Expected array-like (array or non-string sequence), got %r) SparseSeries SparseArrayz1y cannot be class 'SparseSeries' or 'SparseArray'r)TFrr]rcrVNrdzkSupport 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"rer"r'rez -multioutputrrTr{ continuousr()"rr=rrrr>rD __class____name__ryrjrkrlrmr r rnrobytes TypeError IndexErrorrpminr$rVflatrUrsrYrZanyr r rC)rr rrrrvalid sparse_pandasrvrwfirst_row_or_valsuffixrs integral_datas``` rr/r/sZ"/q!1!1B Ax JHQKK J71k3J3J #1c"" "     H1 L   K(,KKMNLMMMQ&%%  N  " " C Cg'@AAA{{ C C@@@@@-z: C C Cq66$$%ABB BBB>BB  C C C C C C C C C C C C C C C C (0 =1aS!!!V991 & . . <  (+66 +X66 /55  ;        vV!!! qw<<" 6Q;;8!!! A;;"17f,,Zq 35O5O,!!! v{{qwqzA~~ zz!'?++ )!!+qvv! $11  66$"))M17;;; < < ) t ; ; ; ;&( ( !!1+0Qa 1$$1=M9N9NQR9R9Rf$$xsO*E1DE1E"!7EE1E""E11E58E5=BH HHc t|dd|td|it|ddBtj|jt |std|d|jnt ||_dSdS)a"Private 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)getattrrDnp array_equalrrR)clfclassess r_check_partial_fit_first_callrssJ%%-'/STTT   3 D ) ) 5>#, g0F0FGG  j18#,,H )11CL4 5rc^g}g}g}|j\}}|tj|}t|r|}tj|j}t|D]}|j|j||j|dz} |2|| } tj |tj | z } nd} |jd||z } tj |j |j||j|dzd\} } tj | | }d| vr|| dkxx| z cc<d| vrC|||jdkr,tj | dd} tj |d| }|| || jd||| z nt|D]}tj |dd|fd\} } || || jdtj | |}||| z |||fS)azCompute 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$rrrtocscdiffindptrrangeindicessumuniquersbincountinsertappend)r sample_weightr n_classes class_priorr n_outputsy_nnzk col_nonzeronz_samp_weightzeros_samp_weight_sum classes_ky_k class_prior_ks rclass_distributionrs,GIK7Iy  =11 {{'D GGII!!y!! D DA)AHQK!(1q5/$ABK(!.{!;(*}(=(=~@V@V(V%%!%() U1X(=%Yqx{QXa!e_45dNIsK^DDDMI~~i1n---1FF--- !!eAh&;&;IiA66 " -rsGG $$$$$$!!!!!!GGGGGGGG>>>>>>>>999999RRRRRRRRRR!$-!%W)W)W)W)W)tE E E P$$$NDDDDN    FG-G-G-G-T*+*+*+*+*+r