:i2:SrSSKJrJr SSKrSSKrSSKJr SSKJ r SSK J r SSKJr SS KJr S S KJrJr S S KJr S S KJr \"\R25r"SS5r"SS\\\S9r"SS\\ 5rSrSr"SS\5r g!\ a SSKJ r Nyf=f)zBase class for sampling)ABCMetaabstractmethodN) BaseEstimator)OneToOneFeatureMixin)_OneToOneFeatureMixin)label_binarize) parse_version)check_classification_targets)check_sampling_strategycheck_target_type)validate_parameter_constraints)ArraysTransformerc\rSrSrSrSrSrg)_ParamsValidationMixinz#Mixin class to validate parameters.c[US5(a7[URURSS9URR S9 gg)a2Validate types and values of constructor parameters. The expected type and values must be defined in the `_parameter_constraints` class attribute, which is a dictionary `param_name: list of constraints`. See the docstring of `validate_parameter_constraints` for a description of the accepted constraints. _parameter_constraintsF)deep) caller_nameN)hasattrrr get_params __class____name__selfs P/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/imblearn/base.py_validate_params'_ParamsValidationMixin._validate_params!sC 41 2 2 *++U+ NN33  3N)r __module__ __qualname____firstlineno____doc__r__static_attributes__r!r rrrs - r rc8\rSrSrSrSrSrSr\S5r Sr g) SamplerMixin1zMixin class for samplers with abstract method. Warning: This class should not be used directly. Use the derive classes instead. samplercxURX5upn[URX R5UlU$UCheck inputs and statistics of the sampler. You should use ``fit_resample`` in all cases. Parameters ---------- X : {array-like, dataframe, sparse matrix} of shape (n_samples, n_features) Data array. y : array-like of shape (n_samples,) Target array. Returns ------- self : object Return the instance itself. ) _check_X_yr sampling_strategy_sampling_typesampling_strategy_rXy_s rfitSamplerMixin.fit:s;&//!'a"9  " "A':':#  r c|[U5 [X5nURX5upn[URX R 5UlURX5nU(a![US[R"U5S9OUSnURUSU5upv[U5S:XaXv4$XvUS4$) Resample the dataset. Parameters ---------- X : {array-like, dataframe, sparse matrix} of shape (n_samples, n_features) Matrix containing the data which have to be sampled. y : array-like of shape (n_samples,) Corresponding label for each sample in X. Returns ------- X_resampled : {array-like, dataframe, sparse matrix} of shape (n_samples_new, n_features) The array containing the resampled data. y_resampled : array-like of shape (n_samples_new,) The corresponding label of `X_resampled`. r classesr) r rr.r r/r0r1 _fit_resamplernpunique transformlenrr3r4arrays_transformer binarize_youtputy_X_s r fit_resampleSamplerMixin.fit_resampleSs* %Q'.q4??10j"9  " "A':':# ##A)@JN6!9biil ;vVWy $--fQi<v;!+xD"&)1DDr cg)a%Base method defined in each sampler to defined the sampling strategy. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Matrix containing the data which have to be sampled. y : array-like of shape (n_samples,) Corresponding label for each sample in X. Returns ------- X_resampled : {ndarray, sparse matrix} of shape (n_samples_new, n_features) The array containing the resampled data. y_resampled : ndarray of shape (n_samples_new,) The corresponding label of `X_resampled`. Nr!)rr3r4s rr=SamplerMixin._fit_resampleys. r )r1N) rr"r#r$r%_estimator_typer6rHrr=r&r!r rr(r(1s.  O2$EL  r r() metaclasscR^\rSrSrSrS SjrS SjrU4SjrU4SjrSr Sr U=r $) BaseSamplerzvBase class for sampling algorithms. Warning: This class should not be used directly. Use the derive classes instead. cXlgNr/)rr/s r__init__BaseSampler.__init__s!2r cVUcSS/n[USS9up$URXSUS9upXU4$)NcsrcscT)indicate_one_vs_all)reset accept_sparse)r _validate_data)rr3r4r[rDs rr.BaseSampler._check_X_ysC  "ENM)!F ""1t="QZr cB>UR5 [TU] X5$r,)rsuperr6rr3r4rs rr6BaseSampler.fits & w{1  r cB>UR5 [TU] X5$)r9)rr_rHr`s rrHBaseSampler.fit_resamples!* w#A))r cS/SQ0$)NX_types)2darraysparse dataframer!rs r _more_tagsBaseSampler._more_tagss=>>r rS)autorR) rr"r#r$r%rTr.r6rHrir& __classcell__rs@rrOrOs& 3 !,*0??r rOcX4$rRr!)r3r4s r _identityros 4Kr c&URS:Xagg)zReturn True if the given estimator is a sampler, False otherwise. Parameters ---------- estimator : object Estimator to test. Returns ------- is_sampler : bool True if estimator is a sampler, otherwise False. r*TF)rL) estimators r is_samplerrrs  I- r cv^\rSrSr%SrSr\S/S/\S/S/S.r\\ S'SSSSS.U4S jjr S r S r S r S rU=r$)FunctionSamplera Construct a sampler from calling an arbitrary callable. Read more in the :ref:`User Guide `. Parameters ---------- func : callable, default=None The callable to use for the transformation. This will be passed the same arguments as transform, with args and kwargs forwarded. If func is None, then func will be the identity function. accept_sparse : bool, default=True Whether sparse input are supported. By default, sparse inputs are supported. kw_args : dict, default=None The keyword argument expected by ``func``. validate : bool, default=True Whether or not to bypass the validation of ``X`` and ``y``. Turning-off validation allows to use the ``FunctionSampler`` with any type of data. .. versionadded:: 0.6 Attributes ---------- sampling_strategy_ : dict Dictionary containing the information to sample the dataset. The keys corresponds to the class labels from which to sample and the values are the number of samples to sample. n_features_in_ : int Number of features in the input dataset. .. versionadded:: 0.9 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during `fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 0.10 See Also -------- sklearn.preprocessing.FunctionTransfomer : Stateless transformer. Notes ----- See :ref:`sphx_glr_auto_examples_applications_plot_outlier_rejections.py` Examples -------- >>> import numpy as np >>> from sklearn.datasets import make_classification >>> from imblearn import FunctionSampler >>> X, y = make_classification(n_classes=2, class_sep=2, ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) We can create to select only the first ten samples for instance. >>> def func(X, y): ... return X[:10], y[:10] >>> sampler = FunctionSampler(func=func) >>> X_res, y_res = sampler.fit_resample(X, y) >>> np.all(X_res == X[:10]) True >>> np.all(y_res == y[:10]) True We can also create a specific function which take some arguments. >>> from collections import Counter >>> from imblearn.under_sampling import RandomUnderSampler >>> def func(X, y, sampling_strategy, random_state): ... return RandomUnderSampler( ... sampling_strategy=sampling_strategy, ... random_state=random_state).fit_resample(X, y) >>> sampler = FunctionSampler(func=func, ... kw_args={'sampling_strategy': 'auto', ... 'random_state': 0}) >>> X_res, y_res = sampler.fit_resample(X, y) >>> print(f'Resampled dataset shape {sorted(Counter(y_res).items())}') Resampled dataset shape [(0, 100), (1, 100)] bypassNboolean)funcr[kw_argsvalidaterTcR>[TU]5 XlX lX0lX@lgrR)r_rTrxr[ryrz)rrxr[ryrzrs rrTFunctionSampler.__init__Ns#  *  r cUR5 UR(a([U5 URXURS9upn[ UR X R5UlU$)r-r[) rrzr r.r[r r/r0r1r2s rr6FunctionSampler.fitUs`&  == ( +ooa$:L:LoMGA!"9  " "A':':#  r cUR5 [X5nUR(a([U5 UR XUR S9upn[ URX R5Ul URX5nUR(a]W(a![US[R"U5S9OUSnURUSU5upv[U5S:XaXv4$XvUS4$U$)aResample the dataset. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Matrix containing the data which have to be sampled. y : array-like of shape (n_samples,) Corresponding label for each sample in X. Returns ------- X_resampled : {array-like, sparse matrix} of shape (n_samples_new, n_features) The array containing the resampled data. y_resampled : array-like of shape (n_samples_new,) The corresponding label of `X_resampled`. r~r r:rr<)rrrzr r.r[r r/r0r1r=rr>r?r@rArBs rrHFunctionSampler.fit_resamplets( .q4 == ( +#q4CUCUV A*"9  " "A':':# ##A) ==vay"))A,?AY  (11&)R@FB"6{a/B8 HbfQi5H H r cURc[O URnU"X40UR(a URO0D6nU$rR)rxrory)rr3r4rxrEs rr=FunctionSampler._fit_resamples6 II-y499aEt||t||E r )r[rxryr1rz)rr"r#r$r%r0callabledictr__annotations__rTr6rHr=r&rlrms@rrtrtsfVpN4 #$<K $D $4PT!!>*Xr rt)!r%abcrrnumpyr>sklearn sklearn.baserr ImportErrorrsklearn.preprocessingrsklearn.utils.fixesr sklearn.utils.multiclassr utilsr r utils._param_validationrutils._validationr __version__sklearn_versionrr(rOrorrrtr!r rrs (&K11-A=C0 3 34&_ )=G_ D@?, 4@?F$wkwyKJKsB BB