UgȥjddlZddlmZmZddlmZmZddlZddl m Z ddl m Z mZmZddlmZmZddlmZddlmZmZmZmZdd lmZmZdd lmZdd lm Z dd l!m"Z"m#Z#dd l$m%Z%m&Z&m'Z'm(Z(m)Z)ddl*m+Z,ddl*m-Z.ddl*m/Z0gdZ1dZ2Gdde eZ3Gddee3eZ4dZ5 ddZ6dS)N)ABCMetaabstractmethod)IntegralReal) BaseEstimatorClassifierMixin _fit_context)ConvergenceWarningNotFittedError) LabelEncoder) check_arraycheck_random_state column_or_1dcompute_class_weight)Interval StrOptions)safe_sparse_dot) available_if)_ovr_decision_functioncheck_classification_targets)_check_large_sparse_check_sample_weight _num_samplescheck_consistent_lengthcheck_is_fitted) _liblinear)_libsvm)_libsvm_sparse)c_svcnu_svc one_class epsilon_svrnu_svrc |jddz}g}tjtjdg|g}t |D]}|||||dzddf}t |dz|D]}|||||dzddf} ||dz ||||dzf} ||||||dzf} |t | |t | | z|S)zGenerate primal coefficients from dual coefficients for the one-vs-one multi class LibSVM in the case of a linear kernel.rrN)shapenpcumsumhstackrangeappendr) dual_coef n_supportsupport_vectorsn_classcoefsv_locsclass1sv1class2sv2alpha1alpha2s P/var/www/surfInsights/venv3-11/lib/python3.11/site-packages/sklearn/svm/_base.py_one_vs_one_coefr:!sAoa 1$G Di A3 "23344G.. U Ugfo 0CCQQQFGFQJ00 U UF!'&/GFQJ4G"G"JKCvz76?WVaZ=P+PPQFvwv!9L'LLMF KK44vs7S7SS T T T T U Kc\eZdZUdZehdegeedddgeddheed ddgeeddd geed dd geed dd geed d d geed ddgd gd geeddd gedhe dgdgeedddgdgdZ e e d<gdZ e dZdZedd)dZdZdZdZdZdZdZd Zd!Zd"Zd#Zd$Zd%Zed&Zd'Z ed(Z!dS)* BaseLibSVMzBase class for estimators that use libsvm as backing library. This implements support vector machine classification and regression. Parameter documentation is in the derived `SVC` class. >rbfpolylinearsigmoid precomputedrNleft)closedscaleautoneitherright?booleanbalancedverbose random_statekerneldegreegammacoef0tolCnuepsilon shrinking probability cache_size class_weightrMmax_iterrO_parameter_constraints)r@r?r>rArBc4|jtvr tdtd|jd||_||_||_||_||_||_||_ ||_ | |_ | |_ | |_ | |_| |_||_||_dS)Nzimpl should be one of z, z was given)_impl LIBSVM_IMPL ValueErrorrQrRrSrTrUrVrWrXrYrZr[r\rMr]rO)selfrQrRrSrTrUrVrWrXrYrZr[r\rMr]rOs r9__init__zBaseLibSVM.__init__fs& :[ ( (*DLL>..00DL r;cbt|dtjdS)zxValidation of y and class_weight. Default implementation for SVR and one-class; overridden in BaseSVC. TwarnF)r)rastyper(r~)rcrs r9rzBaseLibSVM._validate_targetss, AD)))00%0HHHr;cz|jdvsJ|jdkr$tjd|jztdSdS)NrrrznSolver terminated early (max_iter=%i). Consider pre-processing your data with StandardScaler or MinMaxScaler.) fit_status_warningsrr]r rgs r9_warn_from_fit_statusz BaseLibSVM._warn_from_fit_status&s_6))))  q M359]C#       ! r;c t|jrG||_||}|jd|jdkrt dt j|jt j ||fid|d|dt|dtj dd|d |j d |jd |jd |jd |jd|jd|jd|jd|jd|jd|jd|\ |_|_|_|_|_|_|_|_|_ |!dS)Nrrz(X.shape[0] should be equal to X.shape[1]svm_typerr\ class_weight_rQrVrWrZrRrYrUr[rTrSrXr]rv)"r{rQ_BaseLibSVM__Xfit_compute_kernelr'rblibsvmset_verbosity_wraprMrgetattrr(emptyrVrWrZrRrYrUr[rTrrXr]support_support_vectors_ _n_supportrr_probA_probBrrr)rcrrrrrQrvs r9rzBaseLibSVM._dense_fit0s DK  MDK$$Q''AwqzQWQZ'' !KLLL!$,/// J    ![ (-  ! DDD  6  ff ww (( ;; nn   ** ++ LL! "]]# $$ %  M  ! O O O K K   N, ""$$$$$r;chtj|jtjd|_||j|}tj|j tj |j d|j|j |j ||||j|j|j|j|jt'|dtjd||j|j|jt1|jt1|j|j|\ |_|_}|_|_|_ |_!|_"|_#|$tK|drtM|j'dz } nd} |jj d} tj(tj)| | } | stUj+g|_,dStj)d| j-dz| j-| z } tUj+|| | f| | f|_,dS)NrVrlrmrrrr).r(rrr~ sort_indices_sparse_kernelsr libsvm_sparserrMlibsvm_sparse_trainr'indicesindptrrRrrTrUrVrrrWr[rXintrYrZr]rrrrrrrrrrrrtilearangerx csr_matrixrsize) rcrrrrrQrv kernel_typedual_coef_datar0n_SVdual_coef_indicesdual_coef_indptrs r9rzBaseLibSVM._sparse_fit_sAF"*C@@@ *0088 (666  - GAJ F I H   K K J H F D/28A;; 7 7  G O L     ! ! M +   M  !  O O K K   N2 ""$$$ 4 $ $ $-((1,GGG$*1-GBIdOOW==  mB//DOOO!y$)A-/@/E/O   !m!24DEQUDOOOr;cl||}|jr|jn|j}||S)aPerform regression on samples in X. For an one-class model, +1 (inlier) or -1 (outlier) is returned. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train). Returns ------- y_pred : ndarray of shape (n_samples,) The predicted values. )_validate_for_predictr|_sparse_predict_dense_predict)rcrpredicts r9rzBaseLibSVM.predicts<  & &q ) )*.,O$&&D> $, x 7L7L Et**%&  ;- ' 'wqzT_Q/// =wqz4?1#567 "| ! 0C0C0E0ERS0T0TW$.2IWWW r;c|jdkrtd|}tj|rd|jj_n d|j_|S)zWeights assigned to the features when `kernel="linear"`. Returns ------- ndarray of shape (n_features, n_classes) r@z2coef_ is only available when using a linear kernelF)rQAttributeError _get_coefrxryrflags writeablercr1s r9coef_zBaseLibSVM.coef_sc ;( " " !UVV V~~ ;t   )(-DIO % %$)DJ  r;c6t|j|jSN)rrrrgs r9rzBaseLibSVM._get_coefst/1FGGGr;c t|n#t$rtwxYwt|j}|dvr|jStj|jdgS)z)Number of support vectors for each class.rr) rr rrarr`rr(array)rcrs r9rzBaseLibSVM.n_support_s ! D ! ! ! ! ! ! !  !$$TZ00 v  ? "8T_Q/011 1s$r)"r __module__ __qualname____doc__rr{rrrdictr^__annotations__rrrdrhr rrrrrrrrrrrrrpropertyrrrr;r9r=r=As JJJJ K K  8Haf===> J( ) ) HT3V 4 4 4 (4tI>>>?sD;;;<htS$w777 8xc3w7778HT3V<<<=[!{xai@@@A#ZL114>;XhD@@@A'(+$$D6JIIO%)%)^%)N:::\555JJJ65JXIII-%-%-%^;;;z(    D" " " H   <   0! ! ! F&&&PX,HHH 2 2X 2 2 2r;r=) metaclasscReZdZUdZiejeddhgdgdZeed<dD]Z e e e fdZ d Z d Zfd Zd Zeed ZeedZdZdZdZedZedZxZS)BaseSVCz!ABC for LibSVM-based classifiers.ovrovorK)decision_function_shape break_tiesr^)rXrWc||_||_t|||||||d|| | | | | |dS)NrGrP)r r superrd)rcrQrRrSrTrUrVrWrYrZr[r\rMr]r rOr rs r9rdzBaseSVC.__init__sl((?$$ #!%%      r;c`t|d}t|tj|d\}}t |j|||_t|dkrtdt|z||_ tj |tj dS) NTr)return_inverseclassesrrz>The number of classes has to be greater than one; got %d classrVr) rrr(uniquerr\rrrbrrr~)rcry_clss r9rzBaseSVC._validate_targetss !$ ' ' '$Q'''2d333Q1$2CSTVWWW s88a<<Pc((   z!2:S9999r;c||}|jdkr@t|jdkr(t |dk| t|jS|S)a4Evaluate the decision function for the samples in X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2) Returns the decision function of the sample for each class in the model. If decision_function_shape='ovr', the shape is (n_samples, n_classes). Notes ----- If decision_function_shape='ovo', the function values are proportional to the distance of the samples X to the separating hyperplane. If the exact distances are required, divide the function values by the norm of the weight vector (``coef_``). See also `this question `_ for further details. If decision_function_shape='ovr', the decision function is a monotonic transformation of ovo decision function. r rr)rr rrr)rcrdecs r9rzBaseSVC.decision_functions`6%%a((  '5 0 0S5G5G!5K5K)#'C4T]9K9KLL L r;ct||jr|jdkrtd|jrM|jdkrBt |jdkr*t j||d}n!t |}|j t j |t j S)aPerform classification on samples in X. For an one-class model, +1 or -1 is returned. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train) For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train). Returns ------- y_pred : ndarray of shape (n_samples,) Class labels for samples in X. r z>break_ties must be False when decision_function_shape is 'ovo'r rr)axisrp)rr r rbrrr(argmaxrrrtakerintp)rcrrrs r9rzBaseSVC.predicts"  ? t;uDDP  O #,55DM""Q&& $0033!<<">">???r;cb|jstd|jdvrtddS)Nz5predict_proba is not available when probability=Falserwz0predict_proba only implemented for SVC and NuSVCT)rZrr`rgs r9 _check_probazBaseSVC._check_proba4sF  G  :0 0 0 !STT Ttr;c||}|jjdks|jjdkrt d|jr|jn|j}||S)aCompute probabilities of possible outcomes for samples in X. The model needs to have probability information computed at training time: fit with attribute `probability` set to True. Parameters ---------- X : array-like of shape (n_samples, n_features) For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train). Returns ------- T : ndarray of shape (n_samples, n_classes) Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`. Notes ----- The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets. rzApredict_proba is not available when fitted with probability=False)rprobA_rprobB_r r|_sparse_predict_proba_dense_predict_proba)rcr pred_probas r9 predict_probazBaseSVC.predict_proba=sw6  & &q ) ) ; q DK$4$9$9 S +/, UD & &D$>#? k$$$D *11 ""<0000% % % % ^% N : : :@@@@@@J\,"" "H\,-- -:6! ! ! F XXr;r c ddiddddddd iidd id d ddddd iiddddidd}|dkr||S|dkrtd|z||d}|d|z}nH||d}| d|d|d}n&||d}| d|d|d|}n|Std|d|d|d|)aFind the liblinear magic number for the solver. This number depends on the values of the following attributes: - multi_class - penalty - loss - dual The same number is also internally used by LibLinear to determine which solver to use. Fr)FT)l1l2r<Trr )logistic_regressionhinge squared_hingeepsilon_insensitivesquared_epsilon_insensitivecrammer_singerrHr z<`multi_class` must be one of `ovr`, `crammer_singer`, got %rNzloss='%s' is not supportedzThe combination of penalty='z ' and loss='z' is not supportedz' are not supported when dual=zUnsupported set of arguments: z, Parameters: penalty=z, loss=z, dual=)rbget) multi_classpenaltylossdual_solver_type_dict _solver_pen error_string _solver_dual solver_nums r9_get_liblinear_solver_typerSs"(-aj8K8KLLq "!& !12E2EFF $tRj1(,b.C.C'D &&& --    J[ X   $''d33K3d: "w55   77DDD" L &))$55J!!CJ''444QUQUW "! * <<$$$ .  r;r rC皙?c| dvrht}||}|j}t|dkrt d|dzt |||}n"t jdt j}|}tj |t| }|rtdd d }|r|dkrt d |z|}tj |tj |tj |tj|rt#|t j|t j}t j|d }t+||t j}t-| || |}tj||tj||| |||| |t jdj|| \}}t5|}|| krt7jdt:|r|ddddf}||dddfz}n|}d}|||fS)aUsed by Logistic Regression (and CV) and LinearSVC/LinearSVR. Preprocessing is done in this function before supplying it to liblinear. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target vector relative to X C : float Inverse of cross-validation parameter. The lower the C, the higher the penalization. fit_intercept : bool Whether or not to fit an intercept. If set to True, the feature vector is extended to include an intercept term: ``[x_1, ..., x_n, 1]``, where 1 corresponds to the intercept. If set to False, no intercept will be used in calculations (i.e. data is expected to be already centered). intercept_scaling : float Liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the `intercept_scaling` parameter can be set to a value greater than 1; the higher the value of `intercept_scaling`, the lower the impact of regularization on it. Then, the weights become `[w_x_1, ..., w_x_n, w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent the feature weights and the intercept weight is scaled by `intercept_scaling`. This scaling allows the intercept term to have a different regularization behavior compared to the other features. class_weight : dict or 'balanced', default=None Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` penalty : {'l1', 'l2'} The norm of the penalty used in regularization. dual : bool Dual or primal formulation, verbose : int Set verbose to any positive number for verbosity. max_iter : int Number of iterations. tol : float Stopping condition. random_state : int, RandomState instance or None, default=None Controls the pseudo random number generation for shuffling the data. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. multi_class : {'ovr', 'crammer_singer'}, default='ovr' `ovr` trains n_classes one-vs-rest classifiers, while `crammer_singer` optimizes a joint objective over all classes. While `crammer_singer` is interesting from an theoretical perspective as it is consistent it is seldom used in practice and rarely leads to better accuracy and is more expensive to compute. If `crammer_singer` is chosen, the options loss, penalty and dual will be ignored. loss : {'logistic_regression', 'hinge', 'squared_hinge', 'epsilon_insensitive', 'squared_epsilon_insensitive}, default='logistic_regression' The loss function used to fit the model. epsilon : float, default=0.1 Epsilon parameter in the epsilon-insensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set epsilon=0. sample_weight : array-like of shape (n_samples,), default=None Weights assigned to each sample. Returns ------- coef_ : ndarray of shape (n_features, n_features + 1) The coefficient vector got by minimizing the objective function. intercept_ : float The intercept term added to the vector. n_iter_ : array of int Number of iterations run across for each class. )rFrGrzeThis solver needs samples of at least 2 classes in the data, but the data contains only one class: %rrrrpz [LibLinear]rrrsgzqIntercept scaling is %r but needs to be greater than 0. To disable fitting an intercept, set fit_intercept=False.W) requirementsruz@Liblinear failed to converge, increase the number of iterations.NrNrG)r fit_transformrrrbrr(rr~ liblinearrrrrrrxryrrrrequirerrS train_wraprrrrrr )rrrV fit_interceptintercept_scalingr\rKrMrMr]rUrOrJrLrXrency_indrrrbiasr raw_coef_r n_iter_maxrrs r9_fit_liblinearrcsh IIInn!!!$$< x==1  '{+  -\8qQQQ "*555   ))) \ * *C% m$$$$ D%  ! !,.?@  %D g&&&$W---  ))) {1~~A JuBJ / / / 5 5 7 7E Ju3 / / /E(LLLM,['4NNK"-   A    BHSMM%&&  Iw$WJX N    !!!SbS&!&111b5)99  *g %%r;)Nr rCrTN)7rabcrrnumbersrrnumpyr( scipy.sparserrxbaserr r exceptionsr r preprocessingr utilsrrrrutils._param_validationrr utils.extmathrutils.metaestimatorsrutils.multiclassrrutils.validationrrrrrrrrrYrrr rrar:r=r rSrcrr;r9rqst''''''''""""""""??????????;;;;;;;;((((((WWWWWWWWWWWW::::::::++++++//////SSSSSSSS&%%%%% ------GGG @i 2i 2i 2i 2i 2'i 2i 2i 2i 2XsssssozWssssl 666J  !C&C&C&C&C&C&r;