B \N@sVddlmZddlZddlmZddlZddlm Z m Z ddl m Z m Z ddl mZddlmZmZdd lmZdd lmZdd lmZmZmZdd lmZmZdd lmZddlmZddlm Z m!Z!ddlm"Z"ddl#m$Z$ddl%m&Z&ddl%m'Z'dddddgZ(ddZ)Gddde$*e eZ+Gddde$*e e+eZ,dd Z-d&d$d%Z.dS)')print_functionN)ABCMetaabstractmethod)libsvm liblinear) libsvm_sparse) BaseEstimatorClassifierMixin) LabelEncoder)_ovr_decision_function) check_arraycheck_consistent_lengthcheck_random_state) column_or_1d check_X_y)compute_class_weight)safe_sparse_dot)check_is_fitted_check_large_sparse)check_classification_targets)six)ConvergenceWarning)NotFittedErrorc_svcnu_svcZ one_classZ epsilon_svrZnu_svrc Cs|jdd}g}ttdg|g}xt|D]}|||||dddf}xt|d|D]z}|||||dddf} ||d||||df} ||||||df} |t| |t| | qfWq2W|S)zGenerate primal coefficients from dual coefficients for the one-vs-one multi class LibSVM in the case of a linear kernel.rrN)shapenpZcumsumZhstackrangeappendr) Z dual_coefZ n_supportZsupport_vectorsn_classcoefZsv_locsZclass1Zsv1Zclass2Zsv2Zalpha1Zalpha2r#/lib/python3.7/site-packages/sklearn/svm/base.py_one_vs_one_coefs     r%c@seZdZdZdddddgZeddZed d Zd*d d Z ddZ ddZ ddZ ddZ ddZddZddZddZddZd d!Zd"d#Zd$d%Zed&d'Zd(d)Zd S)+ 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. linearZpolyZrbfZsigmoid precomputedcCs|jtkrtdt|jf|dkr0d}t|||_||_||_||_||_||_||_ ||_ | |_ | |_ | |_ | |_| |_||_||_dS)Nz&impl should be one of %s, %s was givenrzXThe gamma value of 0.0 is invalid. Use 'auto' to set gamma to a value of 1 / n_features.)_impl LIBSVM_IMPL ValueErrorkerneldegreegammacoef0tolCnuepsilon shrinking probability cache_size class_weightverbosemax_iter random_state)selfr,r-r.r/r0r1r2r3r4r5r6r7r8r9r:msgr#r#r$__init__Is* zBaseLibSVM.__init__cCs |jdkS)Nr()r,)r;r#r#r$ _pairwisegszBaseLibSVM._pairwiseNc Cst|j}t|}|r*|jdkr*td|o8t|j |_t||t j dddd\}}| |}t j |dkrpgn|t j d}t |j}|d kr|jd |jd krtd d |jd |jd f|jdkr|jd |jd krtd|jd d kr$|jd |jd kr$td|j|jf|jdkr|rR|||d }n|}|jdkr|d krd|jd ||_nd|_nHt|j o|jdk}|rt |dstdtd|jd |_n&|jdkrd|jd |_n|j|_|j} t| rd} |jr |jn|j} |jr:tddd|t dj!} | ||||| | d|j|_"|j#$|_%|j&|_'|jdkrt(|j)d kr|j#d9_#|j& |_&|S)a5Fit the SVM model according to the given training data. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel="precomputed", the expected shape of X is (n_samples, n_samples). y : array-like, shape (n_samples,) Target values (class labels in classification, real numbers in regression) sample_weight : array-like, shape (n_samples,) Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points. Returns ------- self : object Notes ------ If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied. If X is a dense array, then the other methods will not support sparse matrices as input. r(z-Sparse precomputed kernels are not supported.r1csrF)dtypeorder accept_sparseaccept_large_sparseN)r@r rz"X and y have incompatible shapes. zX has %s samples, but y has %s.rz(X.shape[0] should be equal to X.shape[1]zsample_weight and X have incompatible shapes: %r vs %r Note: Sparse matrices cannot be indexed w/boolean masks (use `indices=True` in CV).)scaleZauto_deprecatedrDg?)r'r(zThe default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.autoz[LibSVM])endi) random_seed)rr)*rr:sp isspmatrixr, TypeErrorcallable_sparserrfloat64_validate_targetsasarrayr*indexr)rr+r.ZmultiplyZmeanvar_gammaZisclosewarningswarn FutureWarning _sparse_fit _dense_fitr8printrandintiinfomax shape_fit_ intercept_copy _intercept_ dual_coef_ _dual_coef_lenclasses_) r;Xy sample_weightrndsparse solver_typeZX_varZkernel_uses_gammar,fitZseedr#r#r$rmlsl        &        zBaseLibSVM.fitcCs td|_t|ddtjS)zxValidation of y and class_weight. Default implementation for SVR and one-class; overridden in BaseSVC. rT)rW)rempty class_weight_rZastyperP)r;rhr#r#r$rQs zBaseLibSVM._validate_targetscCs.|jdkst|jdkr*td|jtdS)N)rrrznSolver terminated early (max_iter=%i). Consider pre-processing your data with StandardScaler or MinMaxScaler.) fit_status_AssertionErrorrVrWr9r)r;r#r#r$_warn_from_fit_statuss z BaseLibSVM._warn_from_fit_statuscCst|jr6||_||}|jd|jdkr6tdt|jtj |||||j ||j |j |j |j|j|j|j|j|j|j|j|d\|_|_|_|_|_|_|_|_|dS)Nrrz(X.shape[0] should be equal to X.shape[1])svm_typerir7r,r1r2r5r-r4r0r6r/r.r3r9rI)rNr,_BaseLibSVM__Xfit_compute_kernelrr+rset_verbosity_wrapr8rmror1r2r5r-r4r0r6r/rUr3r9support_support_vectors_ n_support_rcr`probA_probB_rprr)r;rgrhrirlr,rIr#r#r$rZs      ,zBaseLibSVM._dense_fitc Cs,tj|jtjdd|_||j|}t|j t |j d|j|j |j ||||j|j|j|j|j|j||j|j|jt|jt|j|j|\|_|_}|_|_|_|_ |_!|"t#|drt$|j%d} nd} |jj d} t&t'| | } t'd| j(d| j(| } t)*|| | f| | f|_+dS)Nr1)r@rArrfr),rrRdatarP sort_indices_sparse_kernelsrSrrvr8Zlibsvm_sparse_trainrindicesindptrr-rUr/r0r1ror2r6r3intr4r5r9rwrxr`ryrzr{rprrhasattrrerfZtileZarangesizerK csr_matrixrc) r;rgrhrirlr,rI kernel_typeZdual_coef_datar!Zn_SVZdual_coef_indicesZdual_coef_indptrr#r#r$rYs,  $   zBaseLibSVM._sparse_fitcCs$||}|jr|jn|j}||S)aPerform regression on samples in X. For an one-class model, +1 (inlier) or -1 (outlier) is returned. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train). Returns ------- y_pred : array, shape (n_samples,) )_validate_for_predictrO_sparse_predict_dense_predict)r;rgpredictr#r#r$r6s zBaseLibSVM.predictcCs|j\}}||}|jdkr,t|ddd}|j}t|jrpd}|jd|jdkrptd|jd|jdft |j }t j ||j |j|j|j|j|j|j|||j|j|j|jdS) Nrr1F)rArCr(rzMX.shape[1] = %d should be equal to %d, the number of samples at training time)rsr,r-r/r.r6)rrundimrr,rNr_r+r*rSr)rrrwrxryrdrbrzr{r-r/rUr6)r;rg n_samples n_featuresr,rsr#r#r$rIs"       zBaseLibSVM._dense_predictcCs|j}t|rd}|j|}d}t|j|j|j|j j|j j|j j|j j|j t |j ||j|j|j|j||j|j|j|j|j|j|j|jS)Nr(g)r,rNr~rSrZlibsvm_sparse_predictr|rrrxrdrbr*r)r-rUr/r0ror2r3r4r5ryrzr{)r;rgr,rr1r#r#r$r`s"     zBaseLibSVM._sparse_predictcCs@t|jr<|||j}t|r*|}tj|tjdd}|S)z0Return the data transformed by a callable kernelr1)r@rA) rNr,rtrKissparseZtoarrayrrRrP)r;rgr,r#r#r$ruws   zBaseLibSVM._compute_kernelcCsV||}||}|jr&||}n ||}|jdkrRt|jdkrR| S|S)abEvaluates the decision function for the samples in X. Parameters ---------- X : array-like, shape (n_samples, n_features) Returns ------- X : array-like, shape (n_samples, n_class * (n_class-1) / 2) Returns the decision function of the sample for each class in the model. )rrr ) rrurO_sparse_decision_function_dense_decision_functionr)rerfravel)r;rgZdec_funcr#r#r$_decision_functions     zBaseLibSVM._decision_functioncCsht|tjddd}|j}t|r$d}tj||j|j|j |j |j |j |j t|j||j|j|j|jdS)Nr1F)r@rArCr()rsr,r-r6r/r.)rrrPr,rNrdecision_functionrwrxryrdrbrzr{r*rSr)r-r6r/rU)r;rgr,r#r#r$rs   z#BaseLibSVM._dense_decision_functioncCstj|jtjdd|_|j}t|dr*d}|j|}t |j|j |j |j j|j j |j j |j j|jt|j||j|j|j|j|j|j|j|j|j|j|j|j|jS)Nr1)r@rA__call__r()rrRr|rPr,rr~rSrZlibsvm_sparse_decision_functionrrrxrdrbr*r)r-rUr/r0r1ror2r3r4r5ryrzr{)r;rgr,rr#r#r$rs"      z$BaseLibSVM._sparse_decision_functioncCst|dt|dtjddd}|jr8t|s8t|}|jrF|t |rr|jsrt |j srt dt |j|j\}}|j dkr|jd|jd krt d |jd|jd fn$||jdkrt d ||jdf|S) Nrwr?r1F)rBr@rArCz3cannot use sparse input in %r trained on dense datar(rrzMX.shape[1] = %d should be equal to %d, the number of samples at training timezNX.shape[1] = %d should be equal to %d, the number of features at training time)rrrrPrOrKrLrr}rrNr,r+type__name__rr_)r;rgrrr#r#r$rs(     z BaseLibSVM._validate_for_predictcCs<|jdkrtd|}t|r0d|jj_nd|j_|S)Nr'z2coef_ is only available when using a linear kernelF)r,AttributeError _get_coefrKrr|flagsZ writeable)r;r"r#r#r$coef_s   zBaseLibSVM.coef_cCst|j|jS)N)rrdrx)r;r#r#r$rszBaseLibSVM._get_coef)N)r __module__ __qualname____doc__r~rr=propertyr>rmrQrrrZrYrrrrurrrrrrr#r#r#r$r&<s&   x #  r&cseZdZdZefddZddZddZfdd Zd d Z e d d Z ddZ e ddZ ddZddZddZddZZS)BaseSVCz!ABC for LibSVM-based classifiers.cs8||_tt|j|||||||d|| | | | | |ddS)Ng)r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:)decision_function_shapesuperrr=)r;r,r-r.r/r0r1r2r4r5r6r7r8r9rr:) __class__r#r$r=s  zBaseSVC.__init__cCsjt|dd}t|tj|dd\}}t|j|||_t|dkrRtdt|||_ tj |tj ddS)NT)rW)Zreturn_inverser z>The number of classes has to be greater than one; got %d classr1)r@rA) rrruniquerr7rorer+rfrRrP)r;rhZy_clsr#r#r$rQs   zBaseSVC._validate_targetscCs>||}|jdkr:t|jdkr:t|dk| t|jS|S)aEvaluates the decision function for the samples in X. Parameters ---------- X : array-like, shape (n_samples, n_features) Returns ------- X : array-like, shape (n_samples, n_classes * (n_classes-1) / 2) Returns the decision function of the sample for each class in the model. If decision_function_shape='ovr', the shape is (n_samples, n_classes). Notes ------ If decision_function_shape='ovo', the function values are proportional to the distance of the samples X to the separating hyperplane. If the exact distances are required, divide the function values by the norm of the weight vector (``coef_``). See also `this question `_ for further details. ovrr r)rrrerfr )r;rgZdecr#r#r$rs zBaseSVC.decision_functioncs(tt||}|jtj|tjdS)aPerform classification on samples in X. For an one-class model, +1 or -1 is returned. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) For kernel="precomputed", the expected shape of X is [n_samples_test, n_samples_train] Returns ------- y_pred : array, shape (n_samples,) Class labels for samples in X. )r@)rrrrfZtakerrRZintp)r;rgrh)rr#r$r0szBaseSVC.predictcCs$|jstd|jdkr tddS)Nz6predict_proba is not available when probability=False)rrz0predict_proba only implemented for SVC and NuSVC)r5rr))r;r#r#r$ _check_probaGs zBaseSVC._check_probacCs||jS)aCompute probabilities of possible outcomes for samples in X. The model need to have probability information computed at training time: fit with attribute `probability` set to True. Parameters ---------- X : array-like, shape (n_samples, n_features) For kernel="precomputed", the expected shape of X is [n_samples_test, n_samples_train] Returns ------- T : array-like, 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 `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. )r_predict_proba)r;r#r#r$ predict_probaOszBaseSVC.predict_probacCsD||}|jjdks"|jjdkr*td|jr6|jn|j}||S)NrzApredict_proba is not available when fitted with probability=False)rrzrr{rrO_sparse_predict_proba_dense_predict_proba)r;rgZ pred_probar#r#r$rms  zBaseSVC._predict_probacCs||jS)aCompute log probabilities of possible outcomes for samples in X. The model need to have probability information computed at training time: fit with attribute `probability` set to True. Parameters ---------- X : array-like, shape (n_samples, n_features) For kernel="precomputed", the expected shape of X is [n_samples_test, n_samples_train] Returns ------- T : array-like, shape (n_samples, n_classes) Returns the log-probabilities of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute `classes_`. Notes ----- The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets. )r_predict_log_proba)r;r#r#r$predict_log_probavszBaseSVC.predict_log_probacCst||S)N)rlogr)r;rgr#r#r$rszBaseSVC._predict_log_probacCsh||}|j}t|rd}t|j}tj||j|j |j |j |j |j |j|||j|j|j|jd}|S)Nr()rsr,r-r6r/r.)rur,rNr*rSr)rrrwrxryrdrbrzr{r-r6r/rU)r;rgr,rsZpprobr#r#r$rs  zBaseSVC._dense_predict_probacCstj|jtjdd|_|j}t|r(d}|j|}t |j|j |j |j j|j j |j j |j j|jt|j||j|j|j|j|j|j|j|j|j|j|j|j|jS)Nr1)r@rAr()rrRr|rPr,rNr~rSrZlibsvm_sparse_predict_probarrrxrdrbr*r)r-rUr/r0r1ror2r3r4r5ryrzr{)r;rgr,rr#r#r$rs"     zBaseSVC._sparse_predict_probacCs^|jjddkr t|j|j}n:t|j|j|j}t|drPt| }n t |}|S)Nrr) rcrrrxr%ryrKrZvstackZtocsrr)r;r"r#r#r$rs  zBaseSVC._get_coef)rrrrrr=rQrrrrrrrrrrr __classcell__r#r#)rr$rs    rc Csddiddddddd iidd id d ddddd iiddddidd}|dkr\||S|dkrptd|||d}|dkrd|}nJ||d}|dkrd||f}n(||d}|dkrd|||f}n|Std||||fdS)aFind the liblinear magic number for the solver. This number depends on the values of the following attributes: - multi_class - penalty - loss - dual The same number is also internally used by LibLinear to determine which solver to use. Fr)FT)l1l2rTr r )logistic_regressionZhingeZ squared_hingeepsilon_insensitivesquared_epsilon_insensitivecrammer_singerrrz<`multi_class` must be one of `ovr`, `crammer_singer`, got %rNzloss='%s' is not supportedz>The combination of penalty='%s' and loss='%s' is not supportedzLThe combination of penalty='%s' and loss='%s' are not supported when dual=%szJUnsupported set of arguments: %s, Parameters: penalty=%r, loss=%r, dual=%r)r+get) multi_classpenaltylossdualZ_solver_type_dictZ _solver_penZ error_stringZ _solver_dualZ solver_numr#r#r$_get_liblinear_solver_types6          rrr皙?cCs| dkrHt}||}|j}t|dkr:td|dt|||}ntjdtjd}|}t |t | }|r~t dddd }|r|dkrtd |n|}t |t |t |t|rt|tj|tjd}tj|d d }|d krt|jd}ntj|tjdd}t||t| || |}t ||t||| |||| |tdj|| \}}t|}|| krt dt!|r|d d d df}||d d df}n|}d}|||fS)a Used 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}, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target vector relative to X C : float Inverse of cross-validation parameter. Lower the C, the more the penalization. fit_intercept : bool Whether or not to fit the intercept, that is to add a intercept term to the decision function. intercept_scaling : float LibLinear internally penalizes the intercept and this term is subject to regularization just like the other terms of the feature vector. In order to avoid this, one should increase the intercept_scaling. such that the feature vector becomes [x, intercept_scaling]. class_weight : {dict, 'balanced'}, optional 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 : str, {'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, optional (default=None) The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. multi_class : str, {'ovr', 'crammer_singer'} `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 : str, {'logistic_regression', 'hinge', 'squared_hinge', 'epsilon_insensitive', 'squared_epsilon_insensitive} The loss function used to fit the model. epsilon : float, optional (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, optional Weights assigned to each sample. Returns ------- coef_ : ndarray, 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_ : int Maximum number of iterations run across all classes. )rrr zeThis solver needs samples of at least 2 classes in the data, but the data contains only one class: %rr)r@z [LibLinear]rF)rGgzqIntercept scaling is %r but needs to be greater than 0. To disable fitting an intercept, set fit_intercept=False.W)Z requirementsNr1)r@rArHz@Liblinear failed to converge, increase the number of iterations.rJg)"r Z fit_transformrfrer+rrrnrPrrvrr[rrrKrrrRrZrequireZonesrZarrayrrZ train_wraprLr\r]r^rVrWr)rgrhr1Z fit_interceptZintercept_scalingr7rrr8r9r0r:rrr3riencZy_indrfrorjZbiasrlZ raw_coef_Zn_iter_rr`r#r#r$_fit_liblinear s\`              r)NrrrN)/Z __future__rZnumpyrZ scipy.sparserkrKrVabcrrrFrrrbaser r Z preprocessingr Zutils.multiclassr ZutilsrrrrrrZ utils.extmathrZutils.validationrrrZ externalsr exceptionsrrr*r%Zwith_metaclassr&rrrr#r#r#r$s<           !>X>