ZPh4dZddlmZmZddlZddlmZmZddl m Z ddl m Z ddl mZdd lmZmZdd lmZmZmZdd lmZd e vre jd d e vre jd e jd ddl mZejdn #e$rYnwxYwGddeZGddeeeZdZdZGddeZdS)zBase class for sampling)ABCMetaabstractmethodN) BaseEstimatorOneToOneFeatureMixin)label_binarize)METHODS)check_classification_targets)check_sampling_strategycheck_target_type) _fit_contextget_tags validate_data)ArraysTransformer fit_predict fit_transform fit_resample)SIMPLE_METHODSceZdZdZdZeddZeddZedZ dS) SamplerMixinzMixin class for samplers with abstract method. Warning: This class should not be used directly. Use the derive classes instead. samplerT)prefer_skip_nested_validationc z|||\}}}t|j||j|_|S)aCheck 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. **params : dict Extra parameters to use by the sampler. Returns ------- self : object Return the instance itself. ) _check_X_yr sampling_strategy_sampling_typesampling_strategy_)selfXyparams_s M/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/imblearn/base.pyfitzSamplerMixin.fit+sC.//!Q''1a"9  "At':# #  c t|t||}|||\}}}t|j||j|_|j||fi|}|r)t|dtj |n|d}| |d|\}}t|dkr||fn |||dfS)aResample 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. **params : dict Extra parameters to use by the sampler. 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 rrr rrr _fit_resamplernpunique transformlen) rrr r!arrays_transformer binarize_youtputy_X_s r#rzSamplerMixin.fit_resampleHs2 %Q'''.q!44??1a001j"9  "At':# # $#Aq33F33@J XN6!9bill ; ; ; ;vVWy $--fQi<<Bv;;!++Bxx"b&)1DDr%c dS)aBase method defined in each sampler to defined the sampling strategy. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Matrix containing the data which have to be sampled. y : array-like of shape (n_samples,) Corresponding label for each sample in X. **params : dict Extra parameters to use by the sampler. Returns ------- X_resampled : {ndarray, sparse matrix} of shape (n_samples_new, n_features) The array containing the resampled data. y_resampled : ndarray of shape (n_samples_new,) The corresponding label of `X_resampled`. N)rrr r!s r#r*zSamplerMixin._fit_resamplers 4 r%N) __name__ __module__ __qualname____doc___estimator_typer r$rrr*r5r%r#rr"s  O\555658\555'E'E65'ER  ^   r%r) metaclasscHeZdZdZd dZd dZfdZfdZdZd Z xZ S) BaseSamplerzBase class for sampling algorithms. Warning: This class should not be used directly. Use the derive classes instead. autoc||_dSN)r)rrs r#__init__zBaseSampler.__init__s!2r%Ncn|ddg}t|d\}}t|||d|\}}|||fS)NcsrcscT)indicate_one_vs_all)rr reset accept_sparse)r r)rrr rGr0s r#rzBaseSampler._check_X_ysP  "ENM)!FFF :TQ!4}UUU1!Zr%c :tj||fi|S)Check 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. )superr$rrr r! __class__s r#r$zBaseSampler.fits%&uww{1a**6***r%c :tj||fi|S)aResample 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`. )rJrrKs r#rzBaseSampler.fit_resamples&*$uww#Aq33F333r%cdgdiS)NX_types)2darraysparse dataframer5)rs r# _more_tagszBaseSampler._more_tagss===>>r%c ddlm}ddlm}m}m}|d|dddd|}||_d|j_d|j_d|j_ |S)Nr ) TargetTags) InputTags SamplerTagsTagsrT)required)estimator_type target_tagstransformer_tagsregressor_tagsclassifier_tags sampler_tags) utils._sklearn_compatrU utils._tagsrVrWrX input_tags two_d_arrayrQrR)rrUrVrWrXtagss r#__sklearn_tags__zBaseSampler.__sklearn_tags__s555555==========t$" D111! $    $)++&*#!%$(! r%)r>r@) r6r7r8r9rArr$rrSre __classcell__rLs@r#r=r=s 3333    +++++*44444.???r%r=c ||fSr@r5)rr s r# _identityris a4Kr%ct|dr |jdkrdSt|}t|dr |jdSdS)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:rTr_NF)hasattrr:rr_) estimatorrds r# is_samplerrmsZy+,,1Ji1W1Wt I  Dt^$$):)Ft 5r%cjeZdZUdZdZedgdgedgdgdZeed<dddddfd Z d Z d Z d Z xZ S) 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)funcrGkw_argsvalidate_parameter_constraintsTct||_||_||_||_dSr@)rJrArrrGrsrt)rrrrGrsrtrLs r#rAzFunctionSampler.__init__^s:  *   r%c||jr0t|||||j\}}}t |j||j|_|S)rIrG) _validate_paramsrtr rrGr rrr)rrr r"s r#r$zFunctionSampler.fitesu&  = N ( + + +ooa$:LoMMGAq!"9  "At':# #  r%c|t||}|jr0t|||||j\}}}t |j||j|_ | ||}|jrt|r)t|dtj |n|d}||d|\}}t|dkr||fn |||dfS|S)abResample 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`. rxr r'rr))ryrrtr rrGr rrrr*rr+r,r-r.)rrr r/r0r1r2r3s r#rzFunctionSampler.fit_resamples"( .q!44 = W ( + + +#q!4CUVV Aq*"9  "At':# # ##Aq)) = Ivay")A,,????AY  (11&)R@@FB"6{{a//B88b"fQi5H H r%c`|jtn|j}|||fi|jr|jni}|Sr@)rrrirs)rrr rrr1s r#r*zFunctionSampler._fit_resamples? I-yy49aEEt|Ct||EE r%)r6r7r8r9rcallabledictru__annotations__rAr$rr*rfrgs@r#rorosVVpN4 #$<K $$D $4PT!!!!!!!>***Xr%ro) r9abcrrnumpyr+ sklearn.baserrsklearn.preprocessingr sklearn.utils._metadata_requestsrsklearn.utils.multiclassr utilsr r r`r rrutils._validationrappendr ImportErrorrr=rirmror5r%r#rs- ('''''''<<<<<<<<000000444444AAAAAA========HHHHHHHHHH000000GN=!!!'!!GN?###~ ??????N.))))   D j j j j j Wj j j j ZPPPPP, 4mPPPf,wwwwwkwwwwws:BBB