o tBh0*@sdZddlmZmZddlmZddlZddlmZddl m Z m Z ddl m Z ddlmZddlZdd lmZd d lmZd d lmZd d lmZddgZGdddee dZGdddZddZGdddeZddZdddZdS)zUtilities for meta-estimators)ListAny) MethodTypeN)wraps)ABCMetaabstractmethod) attrgetter)update_wrapper)suppress)_safe_indexing) _safe_tags) BaseEstimator available_ifif_delegate_has_methodcsXeZdZUdZeeed<eddZdfdd Z fdd Z d d Z d d Z Z S)_BaseCompositionzJHandles parameter management for classifiers composed of named estimators.stepscCsdSN)selfrrs/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/utils/metaestimators.py__init__sz_BaseComposition.__init__Tc stj|d}|s |St||}z||Wn ttfy$|YSw|D]\}}t|drE|jddD] \}}||d||f<q8q'|S)Ndeep get_paramsTz%s__%s)superrgetattrupdate TypeError ValueErrorhasattritems) rattrrout estimatorsname estimatorkeyvalue __class__rr _get_params s   z_BaseComposition._get_paramsc s||vr t||||t||}t|trN|rNtt)t|\}}t|D]}d|vr>||vr>| ||||q*Wdn1sIwYt j di||S)N__r) setattrpopr isinstancelistr rzipkeys_replace_estimatorr set_params)rr"paramsr! item_names_r%r)rr _set_params6s   z_BaseComposition._set_paramscCsLtt||}t|D]\}\}}||kr||f||<nq t|||dSr)r0r enumerater-)rr"r%new_valnew_estimatorsiestimator_namer7rrrr3Ks z#_BaseComposition._replace_estimatorcCsvtt|t|krtdt|t||jdd}|r)tdt|dd|D}|r9td|dS)Nz$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})lensetrformatr0 intersectionrsorted)rnames invalid_namesrrr_validate_namesTsz _BaseComposition._validate_names)T)__name__ __module__ __qualname____doc__rr__annotations__rrr+r8r3rG __classcell__rrr)rrs     r) metaclassc@s"eZdZdZddZdddZdS)_AvailableIfDescriptoravImplements a conditional property using the descriptor protocol. Using this class to create a decorator will raise an ``AttributeError`` if check(self) returns a falsey value. Note that if check raises an error this will also result in hasattr returning false. See https://docs.python.org/3/howto/descriptor.html for an explanation of descriptors. cCs ||_||_||_t||dSr)fncheckattribute_namer )rrPrQrRrrrrpsz_AvailableIfDescriptor.__init__Ncsbtdt|jdtj|dur#|stj|}|Stjfdd}|S)NzThis z has no attribute cs"|ds j|i|S)Nr)rQrP)argskwargsattr_errrrrr#sz+_AvailableIfDescriptor.__get__..out)AttributeErrorreprrHrRrQrrPr)robjownerr#rrUr__get__xs   z_AvailableIfDescriptor.__get__r)rHrIrJrKrr[rrrrrOes rOcs fddS)afAn attribute that is available only if check returns a truthy value Parameters ---------- check : callable When passed the object with the decorated method, this should return a truthy value if the attribute is available, and either return False or raise an AttributeError if not available. Examples -------- >>> from sklearn.utils.metaestimators import available_if >>> class HelloIfEven: ... def __init__(self, x): ... self.x = x ... ... def _x_is_even(self): ... return self.x % 2 == 0 ... ... @available_if(_x_is_even) ... def say_hello(self): ... print("Hello") ... >>> obj = HelloIfEven(1) >>> hasattr(obj, "say_hello") False >>> obj.x = 2 >>> hasattr(obj, "say_hello") True >>> obj.say_hello() Hello ct||jdSN)rR)rOrHrPrQrrzavailable_if..rr_rr_rrs !cs(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)rrPrdrRr)rrrs z_IffHasAttrDescriptor.__init__c Cs\tdtd}|jD]}z t||}Wn tyYq w|dur&dSt||jdS)Nzrif_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use if_available instead.FT)warningswarn FutureWarningrdrrWrrR)rrYdelegate delegate_namerrrrcs     z_IffHasAttrDescriptor._check)rHrIrJrKrrcrMrrr)rrbs rbcs.ttr tttsffddS)axCreate a decorator for methods that are delegated to a sub-estimator This enables ducktyping by hasattr returning True according to the 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. 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. cr\r])rbrHr^rhrrr`raz(if_delegate_has_method..)r/r0tuplerjrrjrrs   cCst|ddr3t|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. pairwise)r'shapezXPrecomputed kernels or affinity matrices have to be passed as arrays or sparse matrices.rz"X should be a square kernel matrixN)r r rrmnpix_r )r&Xyindices train_indicesX_subsety_subsetrrr _safe_splits ,   rwr) rKtypingrrtypesrre functoolsrabcrroperatorrr numpyro contextlibr utilsr utils._tagsr baser__all__rrOrrbrrwrrrrs(        N*%,