B 0d@sdZddlZddlZddlZddlmZddlZddl m Z m Z m Z m Z ddl mZddl mZmZddl mZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlm Z m!Z!m"Z"ddl#m$Z$m%Z%ddl&m'Z'ddl(m)Z)dddgZ*d)ddZ+ddZ,ddZ-ddZ.Gddde Z/d d!Z0Gd"ddee ee Z1d#d$Z2d%d&Z3Gd'ddee e Z4Gd(ddee e Z5dS)*a Multiclass classification strategies ==================================== This module implements multiclass learning algorithms: - one-vs-the-rest / one-vs-all - one-vs-one - error correcting output codes The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. For example, it is possible to use these estimators to turn a binary classifier or a regressor into a multiclass classifier. It is also possible to use these estimators with multiclass estimators in the hope that their accuracy or runtime performance improves. All classifiers in scikit-learn implement multiclass classification; you only need to use this module if you want to experiment with custom multiclass strategies. The one-vs-the-rest meta-classifier also implements a `predict_proba` method, so long as such a method is implemented by the base classifier. This method returns probabilities of class membership in both the single label and multilabel case. Note that in the multilabel case, probabilities are the marginal probability that a given sample falls in the given class. As such, in the multilabel case the sum of these probabilities over all possible labels for a given sample *will not* sum to unity, as they do in the single label case. N) BaseEstimatorClassifierMixinclone is_classifier)MultiOutputMixin)MetaEstimatorMixin is_regressor) _is_pairwise)LabelBinarizer)euclidean_distances)check_random_state) deprecated) _safe_tags) _num_samples)check_is_fitted)_check_partial_fit_first_callcheck_classification_targets_ovr_decision_function) _safe_split available_if)delayed)ParallelOneVsRestClassifierOneVsOneClassifierOutputCodeClassifiercCsvt|}t|dkr^|dk rN|ddkr0d}n|d}tdt||t||}nt|}||||S)zFit a single binary estimator.rNrz-Label %s is present in all training examples.) npuniquelenwarningswarnstr_ConstantPredictorfitr) estimatorXyclassesZunique_ycr*D/var/www/html/venv/lib/python3.7/site-packages/sklearn/multiclass.py _fit_binaryFs    r,cCs|||td|S)z(Partially fit a single binary estimator.)rr) partial_fitrarray)r%r&r'r*r*r+_partial_fit_binaryYsr/c CsZt|r||Syt||}Wn.ttfk rT||dddf}YnX|S)z1Make predictions using a single binary estimator.Nr)r predictrraveldecision_functionAttributeErrorNotImplementedError predict_proba)r%r&Zscorer*r*r+_predict_binary_s r6cCs t|dst|dstddS)z=Make sure that an estimator implements the necessary methods.r2r5zGThe base estimator should implement decision_function or predict_proba!N)hasattr ValueError)r%r*r*r+_check_estimatorks r9c@s,eZdZddZddZddZddZd S) r#cCs0tddddd}|j||d||fd||_|S)NFT)force_all_finitedtype ensure_2d accept_sparse)resetZvalidate_separately)dict_validate_datay_)selfr&r'Z check_paramsr*r*r+r$vs z_ConstantPredictor.fitcCs0t||j|ddddddt|jt|S)NFT)r:r;r=r<r>)rr@rrepeatrAr)rBr&r*r*r+r0sz_ConstantPredictor.predictcCs0t||j|ddddddt|jt|S)NFT)r:r;r=r<r>)rr@rrCrAr)rBr&r*r*r+r2sz$_ConstantPredictor.decision_functioncCsFt||j|ddddddtjtd|j|jggt|ddS)NFT)r:r;r=r<r>rr)axis)rr@rrCZhstackrAr)rBr&r*r*r+r5sz _ConstantPredictor.predict_probaN)__name__ __module__ __qualname__r$r0r2r5r*r*r*r+r#us   r#cs fddS)zCheck if self.estimator or self.estimators_[0] has attr. If `self.estimators_[0]` has the attr, then its safe to assume that other values has it too. This function is used together with `avaliable_if`. cs&t|jp$t|do$t|jdS)N estimators_r)r7r%rH)rB)attrr*r+s z!_estimators_has..r*)rIr*)rIr+_estimators_hassrKc@seZdZdZddddZddZeedd"d d Zd d Z eed ddZ eedddZ e ddZ e ddZede ddZede ddZede ddZd d!ZdS)#raOne-vs-the-rest (OvR) multiclass strategy. Also known as one-vs-all, this strategy consists in fitting one classifier per class. For each classifier, the class is fitted against all the other classes. In addition to its computational efficiency (only `n_classes` classifiers are needed), one advantage of this approach is its interpretability. Since each class is represented by one and one classifier only, it is possible to gain knowledge about the class by inspecting its corresponding classifier. This is the most commonly used strategy for multiclass classification and is a fair default choice. OneVsRestClassifier can also be used for multilabel classification. To use this feature, provide an indicator matrix for the target `y` when calling `.fit`. In other words, the target labels should be formatted as a 2D binary (0/1) matrix, where [i, j] == 1 indicates the presence of label j in sample i. This estimator uses the binary relevance method to perform multilabel classification, which involves training one binary classifier independently for each label. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object An estimator object implementing :term:`fit` and one of :term:`decision_function` or :term:`predict_proba`. n_jobs : int, default=None The number of jobs to use for the computation: the `n_classes` one-vs-rest problems are computed in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None Attributes ---------- estimators_ : list of `n_classes` estimators Estimators used for predictions. coef_ : ndarray of shape (1, n_features) or (n_classes, n_features) Coefficient of the features in the decision function. This attribute exists only if the ``estimators_`` defines ``coef_``. .. deprecated:: 0.24 This attribute is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). If you use this attribute in :class:`~sklearn.feature_selection.RFE` or :class:`~sklearn.feature_selection.SelectFromModel`, you may pass a callable to the `importance_getter` parameter that extracts feature the importances from `estimators_`. intercept_ : ndarray of shape (1, 1) or (n_classes, 1) If ``y`` is binary, the shape is ``(1, 1)`` else ``(n_classes, 1)`` This attribute exists only if the ``estimators_`` defines ``intercept_``. .. deprecated:: 0.24 This attribute is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). If you use this attribute in :class:`~sklearn.feature_selection.RFE` or :class:`~sklearn.feature_selection.SelectFromModel`, you may pass a callable to the `importance_getter` parameter that extracts feature the importances from `estimators_`. classes_ : array, shape = [`n_classes`] Class labels. n_classes_ : int Number of classes. label_binarizer_ : LabelBinarizer object Object used to transform multiclass labels to binary labels and vice-versa. multilabel_ : boolean Whether a OneVsRestClassifier is a multilabel classifier. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 1.0 See Also -------- MultiOutputClassifier : Alternate way of extending an estimator for multilabel classification. sklearn.preprocessing.MultiLabelBinarizer : Transform iterable of iterables to binary indicator matrix. Examples -------- >>> import numpy as np >>> from sklearn.multiclass import OneVsRestClassifier >>> from sklearn.svm import SVC >>> X = np.array([ ... [10, 10], ... [8, 10], ... [-5, 5.5], ... [-5.4, 5.5], ... [-20, -20], ... [-15, -20] ... ]) >>> y = np.array([0, 0, 1, 1, 2, 2]) >>> clf = OneVsRestClassifier(SVC()).fit(X, y) >>> clf.predict([[-19, -20], [9, 9], [-5, 5]]) array([2, 0, 1]) N)n_jobscCs||_||_dS)N)r%rL)rBr%rLr*r*r+__init__0szOneVsRestClassifier.__init__cstdd_j|}|}jj_dd|jD}tjdfddt|D_ t j ddr~j dj _ t j dd rj dj _ S) aFit underlying estimators. Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data. y : (sparse) array-like of shape (n_samples,) or (n_samples, n_classes) Multi-class targets. An indicator matrix turns on multilabel classification. Returns ------- self : object Instance of fitted estimator. T) sparse_outputcss|]}|VqdS)N)toarrayr1).0colr*r*r+ Msz*OneVsRestClassifier.fit..)rLc3s@|]8\}}ttj|djj|jj|gdVqdS)znot %s)r(N)rr,r%label_binarizer_classes_)rPicolumn)r&rBr*r+rRRs rn_features_in_feature_names_in_) r rSZ fit_transformtocscrTTrrL enumeraterHr7rWrX)rBr&r'Ycolumnsr*)r&rBr+r$4s     zOneVsRestClassifier.fitr-cst|rZtjds&tdjfddtjD_tdd_ j j t t |j rtdt |j j |}|}dd |jD}tjd fd d tj|D_tjd d rjd j_S)aPartially fit underlying estimators. Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iteration. Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data. y : (sparse) array-like of shape (n_samples,) or (n_samples, n_classes) Multi-class targets. An indicator matrix turns on multilabel classification. classes : array, shape (n_classes, ) Classes across all calls to partial_fit. Can be obtained via `np.unique(y_all)`, where y_all is the target vector of the entire dataset. This argument is only required in the first call of partial_fit and can be omitted in the subsequent calls. Returns ------- self : object Instance of partially fitted estimator. r-z3Base estimator {0}, doesn't have partial_fit methodcsg|]}tjqSr*)rr%)rP_)rBr*r+ sz3OneVsRestClassifier.partial_fit..T)rNz;Mini-batch contains {0} while classes must be subset of {1}css|]}|VqdS)N)rOr1)rPrQr*r*r+rRsz2OneVsRestClassifier.partial_fit..)rLc3s"|]\}}tt||VqdS)N)rr/)rPr%rV)r&r*r+rRsrrW)rr7r%r8formatrange n_classes_rHr rSr$rTrr setdiff1drZ transformrYrZrrLziprW)rBr&r'r(r\r]r*)r&rBr+r-es*       zOneVsRestClassifier.partial_fitc CsJt|t|}|jjdkrtj|td}|tj tj |t d}x.r)rDN) rrr.rHrZrZ concatenate multilabel_sumZnewaxis)rBr&r\r*)r&r+r5s z!OneVsRestClassifier.predict_probar2csBt|t|jdkr&|jdStfdd|jDjS)aDecision function for the OneVsRestClassifier. Return the distance of each sample from the decision boundary for each class. This can only be used with estimators which implement the `decision_function` method. Parameters ---------- X : array-like of shape (n_samples, n_features) Input data. Returns ------- T : array-like of shape (n_samples, n_classes) or (n_samples,) for binary classification. Result of calling `decision_function` on the final estimator. .. versionchanged:: 0.19 output shape changed to ``(n_samples,)`` to conform to scikit-learn conventions for binary classification. rrcsg|]}|qSr*)r2r1)rPest)r&r*r+r_sz9OneVsRestClassifier.decision_function..)rrrHr2rr.rZ)rBr&r*)r&r+r2s z%OneVsRestClassifier.decision_functioncCs|jjdS)z(Whether this is a multilabel classifier.Z multilabel)rSrg startswith)rBr*r*r+rwszOneVsRestClassifier.multilabel_cCs t|jS)zNumber of classes.)rrT)rBr*r*r+rbszOneVsRestClassifier.n_classes_zAttribute `coef_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26). If you observe this warning while using RFE or SelectFromModel, use the importance_getter parameter instead.cCsRt|t|jdds tddd|jD}t|drHt|St|S)Nrcoef_z.Base estimator doesn't have a coef_ attribute.cSsg|] }|jqSr*)r{)rPrsr*r*r+r_/sz-OneVsRestClassifier.coef_..)rr7rHr3rqissparsevstackr)rBZcoefsr*r*r+r{#s  zOneVsRestClassifier.coef_zAttribute `intercept_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26). If you observe this warning while using RFE or SelectFromModel, use the importance_getter parameter instead.cCs6t|t|jdds tdtdd|jDS)Nr intercept_z4Base estimator doesn't have an intercept_ attribute.cSsg|]}|jqSr*)r~r1)rPrsr*r*r+r_Bsz2OneVsRestClassifier.intercept_..)rr7rHr3rr.)rBr*r*r+r~6s zOneVsRestClassifier.intercept_zcAttribute `_pairwise` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).cCst|jddS)z@Indicate if wrapped estimator is using a precomputed Gram matrix _pairwiseF)getattrr%)rBr*r*r+rFszOneVsRestClassifier._pairwisecCsdt|jddiS)z@Indicate if wrapped estimator is using a precomputed Gram matrixpairwise)key)rr%)rBr*r*r+ _more_tagsOszOneVsRestClassifier._more_tags)N)rErFrG__doc__rMr$rrKr-r0r5r2propertyrwrbrr{r~rrr*r*r*r+rs(z1  @+(    cCs|t||k||k}||}t|jt}d|||k<d|||k<tt||}t|t||d|dd|||gd|fS)z+Fit a single binary estimator (one-vs-one).rrN)ru)r() r logical_orrhrfrlZarangerr,r)r%r&r'rUjcondy_binaryZindcondr*r*r+_fit_ovo_binaryTs   rcCsRt||k||k}||}t|dkrNt|}d|||k<t||||S|S)z4Partially fit a single binary estimator(one-vs-one).rr)rrrZ zeros_liker/)r%r&r'rUrrrr*r*r+_partial_fit_ovo_binarygs   rc@steZdZdZddddZddZeeddd d Zd d Z d dZ e ddZ e de ddZddZdS)ra| One-vs-one multiclass strategy. This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers, this method is usually slower than one-vs-the-rest, due to its O(n_classes^2) complexity. However, this method may be advantageous for algorithms such as kernel algorithms which don't scale well with `n_samples`. This is because each individual learning problem only involves a small subset of the data whereas, with one-vs-the-rest, the complete dataset is used `n_classes` times. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object An estimator object implementing :term:`fit` and one of :term:`decision_function` or :term:`predict_proba`. n_jobs : int, default=None The number of jobs to use for the computation: the `n_classes * ( n_classes - 1) / 2` OVO problems are computed in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. Attributes ---------- estimators_ : list of ``n_classes * (n_classes - 1) / 2`` estimators Estimators used for predictions. classes_ : numpy array of shape [n_classes] Array containing labels. n_classes_ : int Number of classes. pairwise_indices_ : list, length = ``len(estimators_)``, or ``None`` Indices of samples used when training the estimators. ``None`` when ``estimator``'s `pairwise` tag is False. .. deprecated:: 0.24 The _pairwise attribute is deprecated in 0.24. From 1.1 (renaming of 0.25) and onward, `pairwise_indices_` will use the pairwise estimator tag instead. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- OneVsRestClassifier : One-vs-all multiclass strategy. Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import train_test_split >>> from sklearn.multiclass import OneVsOneClassifier >>> from sklearn.svm import LinearSVC >>> X, y = load_iris(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.33, shuffle=True, random_state=0) >>> clf = OneVsOneClassifier( ... LinearSVC(random_state=0)).fit(X_train, y_train) >>> clf.predict(X_test[:10]) array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1]) N)rLcCs||_||_dS)N)r%rL)rBr%rLr*r*r+rMszOneVsOneClassifier.__init__csjddgdd\tt_tjdkrDtdjjdtt t j dfd d t D}|d_ t}|r|dnd _S) aPFit underlying estimators. Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data. y : array-like of shape (n_samples,) Multi-class targets. Returns ------- self : object The fitted underlying estimator. csrcscF)r=r:rzAOneVsOneClassifier can not be fit when only one class is present.r)rLc 3sD|]<}t|dD](}ttjj|j|VqqdS)rN)rarrr%rT)rPrUr)r& n_classesrBr'r*r+rRsz)OneVsOneClassifier.fit..N)r@rrrrTrr8rflistrdrrLrarHr pairwise_indices_)rBr&r'Zestimators_indicesrr*)r&rrBr'r+r$s"    zOneVsOneClassifier.fitr-cst|}|r6fddtjjddD_ttjr`td t jj ddgd|d \t t tjd}tjd fd d tj|D_d _tjddrjdj_S)aPartially fit underlying estimators. Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iteration, where the first call should have an array of all target variables. Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data. y : array-like of shape (n_samples,) Multi-class targets. classes : array, shape (n_classes, ) Classes across all calls to partial_fit. Can be obtained via `np.unique(y_all)`, where y_all is the target vector of the entire dataset. This argument is only required in the first call of partial_fit and can be omitted in the subsequent calls. Returns ------- self : object The partially fitted underlying estimator. csg|]}tjqSr*)rr%)rPr^)rBr*r+r_sz2OneVsOneClassifier.partial_fit..rz6Mini-batch contains {0} while it must be subset of {1}rrF)r=r:r>)rLc3s6|].\}\}}tt|j|j|VqdS)N)rrrT)rPr%rUr)r&rBr'r*r+rR,sz1OneVsOneClassifier.partial_fit..NrrW)rrarbrHrrrcrTr8r`rr@r itertools combinationsrrLrdrr7rW)rBr&r'r(Z first_callrr*)r&rBr'r+r-s.    zOneVsOneClassifier.partial_fitcCs:||}|jdkr(|j|dktS|j|jddS)aEstimate the best class label for each sample in X. This is implemented as ``argmax(decision_function(X), axis=1)`` which will return the label of the class with most votes by estimators predicting the outcome of a decision for each possible class pair. Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data. Returns ------- y : numpy array of shape [n_samples] Predicted multi-class targets. rrr)rD)r2rbrTZastyperlZargmax)rBr&r\r*r*r+r09s  zOneVsOneClassifier.predictcst||jdddd|j}|dkr:gt|j}nfdd|D}tddt|j|Dj}tddt|j|Dj}t ||t|j }|j d kr|ddd fS|S) a.Decision function for the OneVsOneClassifier. The decision values for the samples are computed by adding the normalized sum of pair-wise classification confidence levels to the votes in order to disambiguate between the decision values when the votes for all the classes are equal leading to a tie. Parameters ---------- X : array-like of shape (n_samples, n_features) Input data. Returns ------- Y : array-like of shape (n_samples, n_classes) or (n_samples,) Result of calling `decision_function` on the final estimator. .. versionchanged:: 0.19 output shape changed to ``(n_samples,)`` to conform to scikit-learn conventions for binary classification. TF)r=r:r>Ncsg|]}dd|fqS)Nr*)rPidx)r&r*r+r_qsz8OneVsOneClassifier.decision_function..cSsg|]\}}||qSr*)r0)rPryXir*r*r+r_tscSsg|]\}}t||qSr*)r6)rPryrr*r*r+r_wsrr) rr@rrrHrr}rdrZrrTrb)rBr&ruZXsZ predictionsZ confidencesr\r*)r&r+r2Os$ z$OneVsOneClassifier.decision_functioncCs t|jS)zNumber of classes.)rrT)rBr*r*r+rb~szOneVsOneClassifier.n_classes_zcAttribute `_pairwise` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).cCst|jddS)z@Indicate if wrapped estimator is using a precomputed Gram matrixrF)rr%)rBr*r*r+rszOneVsOneClassifier._pairwisecCsdt|jddiS)z@Indicate if wrapped estimator is using a precomputed Gram matrixr)r)rr%)rBr*r*r+rszOneVsOneClassifier._more_tags)N)rErFrGrrMr$rrKr-r0r2rrbrrrr*r*r*r+rssN1  @/ c@s2eZdZdZddddddZddZd d ZdS) ra(Error-Correcting) Output-Code multiclass strategy. Output-code based strategies consist in representing each class with a binary code (an array of 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At prediction time, the classifiers are used to project new points in the class space and the class closest to the points is chosen. The main advantage of these strategies is that the number of classifiers used can be controlled by the user, either for compressing the model (0 < code_size < 1) or for making the model more robust to errors (code_size > 1). See the documentation for more details. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object An estimator object implementing :term:`fit` and one of :term:`decision_function` or :term:`predict_proba`. code_size : float, default=1.5 Percentage of the number of classes to be used to create the code book. A number between 0 and 1 will require fewer classifiers than one-vs-the-rest. A number greater than 1 will require more classifiers than one-vs-the-rest. random_state : int, RandomState instance, default=None The generator used to initialize the codebook. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. n_jobs : int, default=None The number of jobs to use for the computation: the multiclass problems are computed in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. Attributes ---------- estimators_ : list of `int(n_classes * code_size)` estimators Estimators used for predictions. classes_ : ndarray of shape (n_classes,) Array containing labels. code_book_ : ndarray of shape (n_classes, code_size) Binary array containing the code of each class. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 1.0 See Also -------- OneVsRestClassifier : One-vs-all multiclass strategy. OneVsOneClassifier : One-vs-one multiclass strategy. References ---------- .. [1] "Solving multiclass learning problems via error-correcting output codes", Dietterich T., Bakiri G., Journal of Artificial Intelligence Research 2, 1995. .. [2] "The error coding method and PICTs", James G., Hastie T., Journal of Computational and Graphical statistics 7, 1998. .. [3] "The Elements of Statistical Learning", Hastie T., Tibshirani R., Friedman J., page 606 (second-edition) 2008. Examples -------- >>> from sklearn.multiclass import OutputCodeClassifier >>> from sklearn.ensemble import RandomForestClassifier >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_samples=100, n_features=4, ... n_informative=2, n_redundant=0, ... random_state=0, shuffle=False) >>> clf = OutputCodeClassifier( ... estimator=RandomForestClassifier(random_state=0), ... random_state=0).fit(X, y) >>> clf.predict([[0, 0, 0, 0]]) array([1]) g?N) code_size random_staterLcCs||_||_||_||_dS)N)r%rrrL)rBr%rrrLr*r*r+rMszOutputCodeClassifier.__init__csvjddjdkr(tdjtjtj}tt _ j j d}|dkrltdt |j}|||f_djjdk<tjdrd jjdk<ndjjdk<d d tj Dt jfd d ttDt dtjdfddtj dD_tjddrRjdj_tjddrrjdj_S)aRFit underlying estimators. Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data. y : array-like of shape (n_samples,) Multi-class targets. Returns ------- self : object Returns a fitted instance of self. Z no_validation)r&r'rz+code_size should be greater than 0, got {0}z=OutputCodeClassifier can not be fit when no class is present.rg?r2rcSsi|]\}}||qSr*r*)rPrUr)r*r*r+ *sz,OutputCodeClassifier.fit..csg|]}j|qSr*) code_book_)rPrU) classes_indexrBr'r*r+r_-sz,OutputCodeClassifier.fit..)r;)rLc3s,|]$}ttjdd|fVqdS)N)rr,r%)rPrU)r&r\rBr*r+rR2sz+OutputCodeClassifier.fit..rWrX)r@rr8r`r9r%r rrrrrTrfrlZ random_samplerr7r[r.rarrrLrHrWrX)rBr&r'rrZ code_size_r*)r&r\rrBr'r+r$s:       $zOutputCodeClassifier.fitcsBt|tfdd|jDj}t||jjdd}|j|S)a1Predict multi-class targets using underlying estimators. Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data. Returns ------- y : ndarray of shape (n_samples,) Predicted multi-class targets. csg|]}t|qSr*)r6)rPrs)r&r*r+r_Jsz0OutputCodeClassifier.predict..r)rD) rrr.rHrZr rZargminrT)rBr&r\rtr*)r&r+r0<s zOutputCodeClassifier.predict)rErFrGrrMr$r0r*r*r*r+rsb?)N)6rr.numpyrr Z scipy.sparsesparserqrbaserrrrrrr r Z preprocessingr Zmetrics.pairwiser utilsr Zutils.deprecationrZ utils._tagsrZutils.validationrrZutils.multiclassrrrZutils.metaestimatorsrrZ utils.fixesrZjoblibr__all__r,r/r6r9r#rKrrrrrr*r*r*r+sP               3  " "