U 3dC!@sdZddlmZmZddlZddlmZmZddlm Z ddl Z ddl m Z ddlmZdd lmZdd lmZd d lmZmZd dgZGdddeedZGdddeZddZdddZdS)zUtilities for meta-estimators)ListAnyN)ABCMetaabstractmethod) attrgetter)suppress)_safe_indexing) _safe_tags) BaseEstimator) available_if_AvailableIfDescriptorr if_delegate_has_methodcsXeZdZUdZeeed<eddZdfdd Z fdd Z d d Z d d Z Z S)_BaseCompositionzJHandles parameter management for classifiers composed of named estimators.ZstepscCsdSN)selfrr@/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/utils/metaestimators.py__init__sz_BaseComposition.__init__Tc stj|d}|s|St||}z||Wnttfk rJ|YSX|D]<\}}t|drP|jddD]\}}||d||f<qrqP|S)Ndeep get_paramsTz%s__%s)superrgetattrupdate TypeError ValueErrorhasattritems) rattrroutZ estimatorsname estimatorkeyvalue __class__rr _get_paramss    z_BaseComposition._get_paramsc s||krt||||t||}t|tr|rttHt|\}}t|D](}d|krT||krT| ||||qTW5QRXt j f||S)N__) setattrpopr isinstancelistrrzipkeys_replace_estimatorrZ set_params)rr paramsrZ item_names_r"r&rr _set_params4s    z_BaseComposition._set_paramscCsLtt||}t|D]$\}\}}||kr||f||<q\sz4_BaseComposition._validate_names..z.Estimator names must not contain __: got {0!r})lensetrformatr- intersectionrsorted)rnames invalid_namesrrr_validate_namesRsz _BaseComposition._validate_names)T)__name__ __module__ __qualname____doc__rr__annotations__rrr(r3r0r@ __classcell__rrr&rrs     r) metaclasscs(eZdZdZfddZddZZS)_IffHasAttrDescriptoratImplements a conditional property using the descriptor protocol. Using this class to create a decorator will raise an ``AttributeError`` if none of the delegates (specified in ``delegate_names``) is an attribute of the base object or the first found delegate does not have an attribute ``attribute_name``. This allows ducktyping of the decorated method based on ``delegate.attribute_name``. Here ``delegate`` is the first item in ``delegate_names`` for which ``hasattr(object, delegate) is True``. See https://docs.python.org/3/howto/descriptor.html for an explanation of descriptors. cst||j|||_dSr)rr_checkdelegate_names)rfnrJattribute_namer&rrrtsz_IffHasAttrDescriptor.__init__c Cshtdtd}|jD]4}zt||}WqLWqtk rHYqYqXq|dkrXdSt||jdS)Nzrif_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.FT)warningswarn FutureWarningrJrAttributeErrorrrL)robjdelegateZ delegate_namerrrrIxs     z_IffHasAttrDescriptor._check)rArBrCrDrrIrFrrr&rrHds rHcs.ttrttts"ffddS)a%Create a decorator for methods that are delegated to a sub-estimator. .. deprecated:: 1.3 `if_delegate_has_method` is deprecated in version 1.1 and will be removed in version 1.3. Use `available_if` instead. This enables ducktyping by hasattr returning True according to the sub-estimator. Parameters ---------- delegate : str, list of str or tuple of str Name of the sub-estimator that can be accessed as an attribute of the base object. If a list or a tuple of names are provided, the first sub-estimator that is an attribute of the base object will be used. Returns ------- callable Callable makes the decorated method available if the delegate has a method with the same name as the decorated method. cst||jdS)N)rL)rHrA)rKrRrrz(if_delegate_has_method..)r,r-tuplerSrrSrrs   cCst|ddrft|dstd|jd|jdkr:td|dkrT|t||}qp|t||}n t||}|dk rt||}nd}||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. Zpairwise)r$shapezXPrecomputed kernels or affinity matrices have to be passed as arrays or sparse matrices.rr z"X should be a square kernel matrixN)r rrrWnpZix_r )r#XyindicesZ train_indicesZX_subsetZy_subsetrrr _safe_splits,    r\)N)rDtypingrrrMabcrroperatorrZnumpyrX contextlibrutilsr Z utils._tagsr baser Z _available_ifr r__all__rrHrr\rrrrs     O,