ZPhodZddlmZddlZddlZddlZddlZddlZddlm Z m Z ddl m Z m Z ddlZddlmZeeejjZdZdZd\d Zeed krd Zd Zn dd lmZddlmZeedkrdefdZdZdZdZnddl mZmZddl!mZmZeedkrPddl"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)ddl"m*Z+ej,dvZ-ddl"m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;ddl"mm+Z+m&Z&m'Z'm(Z(ddl?m.Z.m/Z/m1Z1m2Z2m9Z9m;Z;ddl@m4Z4m7Z7m:Z:ddlAm=Z=m8Z8dd lBm5Z5m6Z6dd!lCm0Z0dd"lDm$Z$m)Z)dd#lm#Z#m-Z-m%Z%dd$l!m3Z3eed%krCdd&lEmFZFd]d(d)d*ZGd+ZH d^d.ZId/ZJd0ZK d_d-d1dd(d(dd(d-d(d2d2dd'd3 d4ZLe d`ieGd5d6ZMe d`ieGd7d8ZNe d`ieGd9d:ZOe d`ieGd;d<ZPe d`ieGd=d>ZQe d`ieGd?d@ZRe d`ieGdAdBZSdCZT dad-ddDdEddFdbdQZUd-ddRdcdSZVdSddTl"mPZPmMZMmQZQmSZSmNZNmOZOe d`ieGdUd6eMZMe d`ieGdVd@ZRe d`ieGdWdBeSZSddXlWmHZHddYlEmUZUmVZVddZlXmGZGdd[l!mKZKmJZJmLZLmIZIdS)da{Ease developer experience to support multiple versions of scikit-learn. This file is intended to be vendored in your project if you do not want to depend on `sklearn-compat` as a package. Then, you can import directly from this file. Be aware that depending on `sklearn-compat` does not add any additional dependencies: we are only depending on `scikit-learn`. Version: 0.1.1 ) annotationsN) dataclassfield)CallableLiteral) parse_versionc.tjdkriSddiS)N) slotsT)sys version_info^/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/imblearn/utils/_sklearn_compat.py_dataclass_argsr"s '!! T?rc ddlm}||S#t$r"ddlm}t |||cYSwxYw)aGet estimator tags in a consistent format across different sklearn versions. This function provides compatibility between sklearn versions before and after 1.6. It returns either a Tags object (sklearn >= 1.6) or a converted Tags object from the dictionary format (sklearn < 1.6) containing metadata about the estimator's requirements and capabilities. Parameters ---------- estimator : estimator object A scikit-learn estimator instance. Returns ------- tags : Tags An object containing metadata about the estimator's requirements and capabilities (e.g., input types, fitting requirements, classifier/regressor specific tags). r)get_tags) _safe_tags)sklearn.utils._tagsr ImportErrorr _to_new_tags) estimatorrrs rrr(sv(>000000x """ >>>222222JJy119=====>s )??ctd|dvd|dvd|dvd|dvd|dvd|dvd|dv|d |d |d d |dv }t|dd|dvd|dv|d|dp|d|d }|7t|dst|drt|d}nd}t |dd}|dkr&t |d|d |d}nd}|d krt |d|d!}nd}|d"kr%t|d#d$%}nd}t||||||||d&d$|d'|d(|d)|d*+ S),zGUtility function convert old tags (dictionary) to new tags (dataclass).1darrayX_types2darray3darraysparse categoricalstringdictrequires_positive_X allow_nanpairwise dataframe) one_d_array two_d_array three_d_arrayrr r!r" positive_onlyr$r%r& requires_y1dlabels2dlabelsrequires_positive_y multioutputmultioutput_only)required one_d_labels two_d_labelsr* multi_output single_outputN transform fit_transformpreserves_dtype)r8_estimator_type classifier poor_score binary_only multilabel)r; multi_class multi_label regressor)r;r?samplersample_indicesF)rBarray_api_support no_validationnon_deterministic requires_fit _skip_test) estimator_type target_tagstransformer_tagsclassifier_tagsregressor_tags sampler_tags input_tagsrCrDrErFrG) InputTags TargetTagshasattrTransformerTagsgetattrClassifierTags RegressorTags SamplerTagsgetTags) old_tagsrrNrIrJrHrKrLrMs rrrFsv)!44)!448I#668I..!Xi%888I.. x * *45;'*%)!44   J,'8I#668I#6645m,L9K0L"#566 K ;''+29o+N+N+$%67    Y(94@@N%%( -$]33 .    $$& - .    """#<<(8%@@    %)'%!#,,':EBB/"#67n-L)   rz1.3c d}|S)zGDecorator to run the fit methods of estimators within context managers.cFtjfd}|S)NcD||g|Ri|SN)_validate_params)rargskwargs fit_methods rwrapperz0_fit_context..decorator..wrappers5**,,,!z)=d===f===r) functoolswraps)rarbs` r decoratorz_fit_context..decorators8 _Z ( ( > > > >) ( >Nrr)prefer_skip_nested_validationres r _fit_contextrgs   rc$ddlm}||S)z(Validate the parameters of an estimator.rvalidate_params)sklearn.utils._param_validationrj)parameter_constraintsrfrjs rrjrjs%CCCCCC4555r)rgriz1.4c|6t|ttfs|g}|fd|DStdrSdt D}t |dkS)aDetermine if an estimator is fitted Parameters ---------- estimator : estimator instance Estimator instance for which the check is performed. attributes : str, list or tuple of str, default=None Attribute name(s) given as string or a list/tuple of strings Eg.: ``["coef_", "estimator_", ...], "coef_"`` If `None`, `estimator` is considered fitted if there exist an attribute that ends with a underscore and does not start with double underscore. all_or_any : callable, {all, any}, default=all Specify whether all or any of the given attributes must exist. Returns ------- fitted : bool Whether the estimator is fitted. Nc0g|]}t|Sr)rQ).0attrrs r z_is_fitted..s#OOODwy$77OOOr__sklearn_is_fitted__cfg|].}|d|d,|/S)___)endswith startswith)rovs rrqz_is_fitted..sK   !**S// BC,,tBTBT    rr) isinstancelisttuplerQrrvarslen)r attributes all_or_any fitted_attrss` r _is_fittedrs0  !j4-88 *(\ :OOOOJOOOPP P 95 6 6 52244 4  I   <  1$$rc &ddlm}|||fddi|S)z$Validate and route input parameters.r)process_routing other_paramsN) sklearn.utils._metadata_requestsr)_obj_methodr`rs rrrs3DDDDDDtWJJ4J6JJJrcddlm}|r|jjd|n |jj}|s$|r$t d|dt |dSdS)zHRaise an error if metadata routing is not enabled and params are passed.r)_routing_enabled.z#Passing extra keyword arguments to z is only supported if enable_metadata_routing=True, which you can set using `sklearn.set_config`. See the User Guide for more details. Extra parameters passed are: N)rr __class____name__ ValueErrorset)paramsownermethodrcallers r_raise_for_paramsrsEEEEEE *u' 2 2& 2 2 2)   !! f HfHH;>f++ HH     rcv tjd}n#t$rYdSwxYwt||jS)z+Return True if the X is a pandas dataframe.pandasF)r modulesKeyErrorry DataFrame)Xpds r _is_pandas_dfrsI X&BB   55 !R\***s  ##)rr)rrz1.5) _IS_32BIT_approximate_mode#_in_unstable_openblas_configuration gen_batchesgen_even_slicesget_chunk_n_rowssafe_sqr)_chunk_generator)wasm32wasm64)_determine_key_type_get_column_indices_print_elapsed_time _safe_assign_safe_indexing_to_object_arrayaxis0_safe_slicecheck_matplotlib_supportcheck_pandas_supportindices_to_mask is_scalar_nanresample safe_maskshuffle) _is_pandas_na)chunk_generatorrrr)rrrrrr)rrr) is_pandas_nar)rr)r)rr)r_IS_WASMr)rz1.6_construct_instanceF) raise_unknowncRddlm}fd}|}||S)Nrtype_of_targetcNr!|dkrrnd}td|d|S)zlDepending on the value of raise_unknown, either raise an error or return 'unknown'. unknowndatazUnknown label type for z: )r) target_typeinput input_namerys r_raise_or_returnz(type_of_target.._raise_or_returnxsJ # !9!9&0< f !I5!I!IA!I!IJJJ""r)r)sklearn.utils.multiclassr)rrrrrrs``` rrrtsa;;;;;; # # # # # # #%nQ:>>>  ,,,rc#,Kt|VdSr]r) Estimators r_construct_instancesrs !),,,,,,,rrDTc H|rrt|||t|||t|to|dk}|dupt|to|dk}|s|r|} n |r|s|} n||f} | Sd|vr|d} nd} |jd||||| d|S)aValidate input data and set or check feature names and counts of the input. See the original scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.utils.validation.validate_data.html#sklearn.utils.validation.validate_data resetrDNensure_all_finiteT)rrrvalidate_separatelyforce_all_finiter)_check_n_features_check_feature_namesrystrpop_validate_data) _estimatorrrrrskip_check_arrayr`no_val_Xno_val_youtrs r validate_datars    j!5 9 9 9 9 Qe < < < <!!S))Ba?.BHDyOJq#$6$6$O1;OH   ( dJ"f,,#)::.A#B#B  #' ,:,$7!1    rc0|||S)zHSet the `n_features_in_` attribute, or check against it on an estimator.r)rrrrs rrrs**1E*:::rc0|||S)z4Check `input_features` and generate names if needed.r)rrs rrrs--au-===rnumeric) accept_large_sparsedtypeordercopyforce_writeablerensure_non_negative ensure_2dallow_ndensure_min_samplesensure_min_featuresrrc ddlm}||}nd}tj|j}i}d|vr||d<d|vr||d<||f||||||| | | | | |d |S)zInput validation on an array, list, sparse matrix or similar. Check the original documentation for more details: https://scikit-learn.org/stable/modules/generated/sklearn.utils.check_array.html r) check_arrayNTrr) accept_sparserrrrrrrrrrr)sklearn.utils.validationrinspect signature parameters)arrayrrrrrrrrrrrrrr _check_arrayrcheck_array_paramsr`s rrrs. IHHHHH  (0  # $.|<<G  2 2 2(7F$ % $6 6 6,?F( )|  ' 3-1 3!     rceZdZUdZdZded<dZded<dZded<dZded<dZ ded <dZ ded <dZ ded <dZ ded <dZ ded <dZded<dZded<dS)rOa5Tags for the input data. Parameters ---------- one_d_array : bool, default=False Whether the input can be a 1D array. two_d_array : bool, default=True Whether the input can be a 2D array. Note that most common tests currently run only if this flag is set to ``True``. three_d_array : bool, default=False Whether the input can be a 3D array. sparse : bool, default=False Whether the input can be a sparse matrix. categorical : bool, default=False Whether the input can be categorical. string : bool, default=False Whether the input can be an array-like of strings. dict : bool, default=False Whether the input can be a dictionary. positive_only : bool, default=False Whether the estimator requires positive X. allow_nan : bool, default=False Whether the estimator supports data with missing values encoded as `np.nan`. pairwise : bool, default=False This boolean attribute indicates whether the data (`X`), :term:`fit` and similar methods consists of pairwise measures over samples rather than a feature representation for each sample. It is usually `True` where an estimator has a `metric` or `affinity` or `kernel` parameter with value 'precomputed'. Its primary purpose is to support a :term:`meta-estimator` or a cross validation procedure that extracts a sub-sample of data intended for a pairwise estimator, where the data needs to be indexed on both axes. Specifically, this tag is used by `sklearn.utils.metaestimators._safe_split` to slice rows and columns. Fboolr'Tr(r)rr r!r"r*r$r%r&N)r __module__ __qualname____doc__r'__annotations__r(r)rr r!r"r*r$r%r&rrrrOrOs- - ^" !!!!     # ####! !!!!# ####  rrOcdeZdZUdZded<dZded<dZded<dZded<dZded<d Z ded <d S) rPaTags for the target data. Parameters ---------- required : bool Whether the estimator requires y to be passed to `fit`, `fit_predict` or `fit_transform` methods. The tag is ``True`` for estimators inheriting from `~sklearn.base.RegressorMixin` and `~sklearn.base.ClassifierMixin`. one_d_labels : bool, default=False Whether the input is a 1D labels (y). two_d_labels : bool, default=False Whether the input is a 2D labels (y). positive_only : bool, default=False Whether the estimator requires a positive y (only applicable for regression). multi_output : bool, default=False Whether a regressor supports multi-target outputs or a classifier supports multi-class multi-output. single_output : bool, default=True Whether the target can be single-output. This can be ``False`` if the estimator supports only multi-output cases. rr1Fr2r3r*r4Tr5N) rrrrrr2r3r*r4r5rrrrPrP/s|  : " """"" """"# ####" """"" """"""rrPc8eZdZUdZedZded<dS)rRaTags for the transformer. Parameters ---------- preserves_dtype : list[str], default=["float64"] Applies only on transformers. It corresponds to the data types which will be preserved such that `X_trans.dtype` is the same as `X.dtype` after calling `transformer.transform(X)`. If this list is empty, then the transformer is not expected to preserve the data type. The first value in the list is considered as the default data type, corresponding to the data type of the output when the input data type is not going to be preserved. cdgS)Nfloat64rrrrzTransformerTags.fsI;rdefault_factoryz list[str]r8N)rrrrrr8rrrrrRrRUs@ &+U;N;N%O%O%OOOOOOOrrRc>eZdZUdZdZded<dZded<dZded<dS) rTapTags for the classifier. Parameters ---------- poor_score : bool, default=False Whether the estimator fails to provide a "reasonable" test-set score, which currently for classification is an accuracy of 0.83 on ``make_blobs(n_samples=300, random_state=0)``. The datasets and values are based on current estimators in scikit-learn and might be replaced by something more systematic. multi_class : bool, default=True Whether the classifier can handle multi-class classification. Note that all classifiers support binary classification. Therefore this flag indicates whether the classifier is a binary-classifier-only or not. multi_label : bool, default=False Whether the classifier supports multi-label output. Frr;Tr>r?N)rrrrr;rr>r?rrrrTrThsQ  *!          ! !!!!!!rrTc0eZdZUdZdZded<dZded<dS)rUa}Tags for the regressor. Parameters ---------- poor_score : bool, default=False Whether the estimator fails to provide a "reasonable" test-set score, which currently for regression is an R2 of 0.5 on ``make_regression(n_samples=200, n_features=10, n_informative=1, bias=5.0, noise=20, random_state=42)``. The dataset and values are based on current estimators in scikit-learn and might be replaced by something more systematic. multi_label : bool, default=False Whether the regressor supports multilabel output. Frr;r?N)rrrrr;rr?rrrrUrUs@  !     ! !!!!!!rrUc"eZdZUdZdZded<dS)rVzTags for the sampler. Parameters ---------- sample_indices : bool, default=False Whether the sampler returns the indices of the samples that were selected. FrrBN)rrrrrBrrrrrVrVs/   %$$$$$$rrVceZdZUdZded<ded<dZded<dZd ed <dZd ed <d Zded<d Z ded<d Z ded<dZ ded<d Z ded<e eZded<dZded<dS)rXaFTags for the estimator. See :ref:`estimator_tags` for more information. Parameters ---------- estimator_type : str or None The type of the estimator. Can be one of: - "classifier" - "regressor" - "transformer" - "clusterer" - "outlier_detector" - "density_estimator" target_tags : :class:`TargetTags` The target(y) tags. transformer_tags : :class:`TransformerTags` or None The transformer tags. classifier_tags : :class:`ClassifierTags` or None The classifier tags. regressor_tags : :class:`RegressorTags` or None The regressor tags. array_api_support : bool, default=False Whether the estimator supports Array API compatible inputs. no_validation : bool, default=False Whether the estimator skips input-validation. This is only meant for stateless and dummy transformers! non_deterministic : bool, default=False Whether the estimator is not deterministic given a fixed ``random_state``. requires_fit : bool, default=True Whether the estimator requires to be fitted before calling one of `transform`, `predict`, `predict_proba`, or `decision_function`. _skip_test : bool, default=False Whether to skip common tests entirely. Don't use this unless you have a *very good* reason. input_tags : :class:`InputTags` The input data(X) tags. z str | NonerHrPrINzTransformerTags | NonerJzClassifierTags | NonerKzRegressorTags | NonerLFrrCrDrETrFrGrrOrNSamplerTags | NonerM)rrrrrrJrKrLrCrDrErFrGrrOrNrMrrrrXrXs/ / b #"""377777155555/33333"'''''# ####"'''''! !!!!     %i @ @ @ @@@@+/ //////rrXcz|jjfd}||j_tj|||_|S)NcL|}|di|S)zEInstance-level _more_tags that combines class tags with _xfail_checks _xfail_checks)update)selftagsexpected_failed_checksoriginal_class_more_tagss rpatched_instance_more_tagsz6_patched_more_tags..patched_instance_more_tagss2,+D11D KK*@A B B BKr)r _more_tagstypes MethodType)rr rrs ` @r_patched_more_tagsrsX#,#6#A       *D &$/0JIVV rwarnraise)legacyr on_skipon_failcallbackrrr dict[str, str] | NonerLiteral['warn'] | NonerLiteral['raise', 'warn'] | NonerCallable | NonecDddlm}|t|||S)Nr)check_estimator) generate_only)sklearn.utils.estimator_checksrr)rr rr rrrrs rrrsA CBBBBB y*@ A A'    r)rr cBddlm}fd|D}||S)Nr)parametrize_with_checkscBg|]}t||Sr)r)rorr s rrqz+parametrize_with_checks..s?    y*@*@*K*K L L   r)r!r#) estimatorsrr r#s ` rr#r#sS KJJJJJ    '   '&z222r)rTrOrUrXrPrRceZdZUdZded<dS)rOTrr&N)rrrr&rrrrrOrO#s# rceZdZUdZded<dS)rVFrrBN)rrrrBrrrrrVrV's#$$$$$$$rceZdZUdZded<dS)rXNrrM)rrrrMrrrrrXrX+s#+/ //////r)r)rr#r)rrrrr])r)rDrDTFF)Fr)NF) rrr rrrrrrr)rrr r)Yr __future__rrcrplatformr r dataclassesrrtypingrrsklearnsklearn.utils.fixesr __version__ base_versionsklearn_versionrrrrgrj sklearn.baserkallrrrrsklearn.utils.metadata_routingr sklearn.utilsrrrrrrrrrmachinerrrrrrrrrrrrrrrrrsklearn.utils._chunkingsklearn.utils._indexingsklearn.utils._masksklearn.utils._missing$sklearn.utils._optional_dependenciessklearn.utils._user_interfacesklearn.utils.extmathr!rrrrrrrrOrPrRrTrUrVrXrrr#-sklearn.utils._test_common.instance_generatorrrrrr?sS   #""""" (((((((($$$$$$$$------- g.A B B OPP >>>>> 4 ! !!4 4 4 4 4 nY##""##: : : : : : : $#: xY##""################$###JY##""##PPPPPPP$#P$Y##""##"""""""$#"4Y##""##"""""""$#"(Y##""## % % % % % % %$# %Y##""##=0=0=0=0=0=0=0$#=0~" 8<*03:$(      *26 33333333&Y##""##I$#Y##""##%%%%%%%$#%Y##""##00000t00$#0877777r