o Rh@sdZddlmZmZddlmZddlmZmZddl Z ddl m Z ddl mZdd lmZd d lmZd gZGd dde edZdddZdS)zUtilities for meta-estimators.)ABCMetaabstractmethod)suppress)AnyListN) BaseEstimator)_safe_indexing)get_tags) available_ifr csXeZdZUdZeeed<eddZdfdd Z fdd Z d d Z d d Z Z S)_BaseCompositionzJHandles parameter management for classifiers composed of named estimators.stepscCsdSN)selfrr^/home/air/sanwanet/backup_V2/venv/lib/python3.10/site-packages/sklearn/utils/metaestimators.py__init__sz_BaseComposition.__init__Tc stj|d}|s |St||}z||Wn ttfy$|YSw|D]\}}t|drE|jddD] \}}||d||f<q8q'|S)Ndeep get_paramsTz%s__%s)superrgetattrupdate TypeError ValueErrorhasattritems) rattrrout estimatorsname estimatorkeyvalue __class__rr _get_paramss   z_BaseComposition._get_paramsc s||vr t||||t||}t|trN|rNtt)t|\}}t|D]}d|vr>||vr>| ||||q*Wdn1sIwYt j di||S)N__r) setattrpopr isinstancelistrrzipkeys_replace_estimatorr set_params)rrparamsr item_names_r!r%rr _set_params3s   z_BaseComposition._set_paramscCsLtt||}t|D]\}\}}||kr||f||<nq t|||dSr)r,r enumerater))rrr!new_valnew_estimatorsiestimator_namer3rrrr/Hs z#_BaseComposition._replace_estimatorcCsvtt|t|krtdt|t||jdd}|r)tdt|dd|D}|r9td|dS)Nz$Names provided are not unique: {0!r}Frz:Estimator names conflict with constructor arguments: {0!r}cSsg|]}d|vr|qS)r(r).0r!rrr [sz4_BaseComposition._validate_names..z.Estimator names must not contain __: got {0!r})lensetrformatr, intersectionrsorted)rnames invalid_namesrrr_validate_namesQsz _BaseComposition._validate_names)T)__name__ __module__ __qualname____doc__rr__annotations__rrr'r4r/rC __classcell__rrr%rr s     r ) metaclasscCst|jjr3t|dstd|jd|jdkrtd|dur*|t||}n|t||}nt||}|durEt||}||fSd}||fS)aCreate subset of dataset and properly handle kernels. Slice X, y according to indices for cross-validation, but take care of precomputed kernel-matrices or pairwise affinities / distances. If ``estimator._pairwise is True``, X needs to be square and we slice rows and columns. If ``train_indices`` is not None, we slice rows using ``indices`` (assumed the test set) and columns using ``train_indices``, indicating the training set. Labels y will always be indexed only along the first axis. Parameters ---------- estimator : object Estimator to determine whether we should slice only rows or rows and columns. X : array-like, sparse matrix or iterable Data to be indexed. If ``estimator._pairwise is True``, this needs to be a square array-like or sparse matrix. y : array-like, sparse matrix or iterable Targets to be indexed. indices : array of int Rows to select from X and y. If ``estimator._pairwise is True`` and ``train_indices is None`` then ``indices`` will also be used to slice columns. train_indices : array of int or None, default=None If ``estimator._pairwise is True`` and ``train_indices is not None``, then ``train_indices`` will be use to slice the columns of X. Returns ------- X_subset : array-like, sparse matrix or list Indexed data. y_subset : array-like, sparse matrix or list Indexed targets. shapezXPrecomputed kernels or affinity matrices have to be passed as arrays or sparse matrices.rr z"X should be a square kernel matrixN) r input_tagspairwiserrrKnpix_r )r"Xyindices train_indicesX_subsety_subsetrrr _safe_splitbs ,   rVr)rGabcrr contextlibrtypingrrnumpyrNbaserutilsr utils._tagsr _available_ifr __all__r rVrrrrs     N